diff --git a/Lib/pydoc_data/topics.py b/Lib/pydoc_data/topics.py
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--- a/Lib/pydoc_data/topics.py
+++ b/Lib/pydoc_data/topics.py
@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-# Autogenerated by Sphinx on Sat May 30 16:57:51 2015
+# Autogenerated by Sphinx on Sat Jul  4 19:11:05 2015
 topics = {'assert': u'\nThe "assert" statement\n**********************\n\nAssert statements are a convenient way to insert debugging assertions\ninto a program:\n\n   assert_stmt ::= "assert" expression ["," expression]\n\nThe simple form, "assert expression", is equivalent to\n\n   if __debug__:\n      if not expression: raise AssertionError\n\nThe extended form, "assert expression1, expression2", is equivalent to\n\n   if __debug__:\n      if not expression1: raise AssertionError(expression2)\n\nThese equivalences assume that "__debug__" and "AssertionError" refer\nto the built-in variables with those names.  In the current\nimplementation, the built-in variable "__debug__" is "True" under\nnormal circumstances, "False" when optimization is requested (command\nline option -O).  The current code generator emits no code for an\nassert statement when optimization is requested at compile time.  Note\nthat it is unnecessary to include the source code for the expression\nthat failed in the error message; it will be displayed as part of the\nstack trace.\n\nAssignments to "__debug__" are illegal.  The value for the built-in\nvariable is determined when the interpreter starts.\n',
  'assignment': u'\nAssignment statements\n*********************\n\nAssignment statements are used to (re)bind names to values and to\nmodify attributes or items of mutable objects:\n\n   assignment_stmt ::= (target_list "=")+ (expression_list | yield_expression)\n   target_list     ::= target ("," target)* [","]\n   target          ::= identifier\n              | "(" target_list ")"\n              | "[" target_list "]"\n              | attributeref\n              | subscription\n              | slicing\n              | "*" target\n\n(See section *Primaries* for the syntax definitions for\n*attributeref*, *subscription*, and *slicing*.)\n\nAn assignment statement evaluates the expression list (remember that\nthis can be a single expression or a comma-separated list, the latter\nyielding a tuple) and assigns the single resulting object to each of\nthe target lists, from left to right.\n\nAssignment is defined recursively depending on the form of the target\n(list). When a target is part of a mutable object (an attribute\nreference, subscription or slicing), the mutable object must\nultimately perform the assignment and decide about its validity, and\nmay raise an exception if the assignment is unacceptable.  The rules\nobserved by various types and the exceptions raised are given with the\ndefinition of the object types (see section *The standard type\nhierarchy*).\n\nAssignment of an object to a target list, optionally enclosed in\nparentheses or square brackets, is recursively defined as follows.\n\n* If the target list is a single target: The object is assigned to\n  that target.\n\n* If the target list is a comma-separated list of targets: The\n  object must be an iterable with the same number of items as there\n  are targets in the target list, and the items are assigned, from\n  left to right, to the corresponding targets.\n\n  * If the target list contains one target prefixed with an\n    asterisk, called a "starred" target: The object must be a sequence\n    with at least as many items as there are targets in the target\n    list, minus one.  The first items of the sequence are assigned,\n    from left to right, to the targets before the starred target.  The\n    final items of the sequence are assigned to the targets after the\n    starred target.  A list of the remaining items in the sequence is\n    then assigned to the starred target (the list can be empty).\n\n  * Else: The object must be a sequence with the same number of\n    items as there are targets in the target list, and the items are\n    assigned, from left to right, to the corresponding targets.\n\nAssignment of an object to a single target is recursively defined as\nfollows.\n\n* If the target is an identifier (name):\n\n  * If the name does not occur in a "global" or "nonlocal" statement\n    in the current code block: the name is bound to the object in the\n    current local namespace.\n\n  * Otherwise: the name is bound to the object in the global\n    namespace or the outer namespace determined by "nonlocal",\n    respectively.\n\n  The name is rebound if it was already bound.  This may cause the\n  reference count for the object previously bound to the name to reach\n  zero, causing the object to be deallocated and its destructor (if it\n  has one) to be called.\n\n* If the target is a target list enclosed in parentheses or in\n  square brackets: The object must be an iterable with the same number\n  of items as there are targets in the target list, and its items are\n  assigned, from left to right, to the corresponding targets.\n\n* If the target is an attribute reference: The primary expression in\n  the reference is evaluated.  It should yield an object with\n  assignable attributes; if this is not the case, "TypeError" is\n  raised.  That object is then asked to assign the assigned object to\n  the given attribute; if it cannot perform the assignment, it raises\n  an exception (usually but not necessarily "AttributeError").\n\n  Note: If the object is a class instance and the attribute reference\n  occurs on both sides of the assignment operator, the RHS expression,\n  "a.x" can access either an instance attribute or (if no instance\n  attribute exists) a class attribute.  The LHS target "a.x" is always\n  set as an instance attribute, creating it if necessary.  Thus, the\n  two occurrences of "a.x" do not necessarily refer to the same\n  attribute: if the RHS expression refers to a class attribute, the\n  LHS creates a new instance attribute as the target of the\n  assignment:\n\n     class Cls:\n         x = 3             # class variable\n     inst = Cls()\n     inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3\n\n  This description does not necessarily apply to descriptor\n  attributes, such as properties created with "property()".\n\n* If the target is a subscription: The primary expression in the\n  reference is evaluated.  It should yield either a mutable sequence\n  object (such as a list) or a mapping object (such as a dictionary).\n  Next, the subscript expression is evaluated.\n\n  If the primary is a mutable sequence object (such as a list), the\n  subscript must yield an integer.  If it is negative, the sequence\'s\n  length is added to it.  The resulting value must be a nonnegative\n  integer less than the sequence\'s length, and the sequence is asked\n  to assign the assigned object to its item with that index.  If the\n  index is out of range, "IndexError" is raised (assignment to a\n  subscripted sequence cannot add new items to a list).\n\n  If the primary is a mapping object (such as a dictionary), the\n  subscript must have a type compatible with the mapping\'s key type,\n  and the mapping is then asked to create a key/datum pair which maps\n  the subscript to the assigned object.  This can either replace an\n  existing key/value pair with the same key value, or insert a new\n  key/value pair (if no key with the same value existed).\n\n  For user-defined objects, the "__setitem__()" method is called with\n  appropriate arguments.\n\n* If the target is a slicing: The primary expression in the\n  reference is evaluated.  It should yield a mutable sequence object\n  (such as a list).  The assigned object should be a sequence object\n  of the same type.  Next, the lower and upper bound expressions are\n  evaluated, insofar they are present; defaults are zero and the\n  sequence\'s length.  The bounds should evaluate to integers. If\n  either bound is negative, the sequence\'s length is added to it.  The\n  resulting bounds are clipped to lie between zero and the sequence\'s\n  length, inclusive.  Finally, the sequence object is asked to replace\n  the slice with the items of the assigned sequence.  The length of\n  the slice may be different from the length of the assigned sequence,\n  thus changing the length of the target sequence, if the target\n  sequence allows it.\n\n**CPython implementation detail:** In the current implementation, the\nsyntax for targets is taken to be the same as for expressions, and\ninvalid syntax is rejected during the code generation phase, causing\nless detailed error messages.\n\nAlthough the definition of assignment implies that overlaps between\nthe left-hand side and the right-hand side are \'simultanenous\' (for\nexample "a, b = b, a" swaps two variables), overlaps *within* the\ncollection of assigned-to variables occur left-to-right, sometimes\nresulting in confusion.  For instance, the following program prints\n"[0, 2]":\n\n   x = [0, 1]\n   i = 0\n   i, x[i] = 1, 2         # i is updated, then x[i] is updated\n   print(x)\n\nSee also: **PEP 3132** - Extended Iterable Unpacking\n\n     The specification for the "*target" feature.\n\n\nAugmented assignment statements\n===============================\n\nAugmented assignment is the combination, in a single statement, of a\nbinary operation and an assignment statement:\n\n   augmented_assignment_stmt ::= augtarget augop (expression_list | yield_expression)\n   augtarget                 ::= identifier | attributeref | subscription | slicing\n   augop                     ::= "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="\n             | ">>=" | "<<=" | "&=" | "^=" | "|="\n\n(See section *Primaries* for the syntax definitions of the last three\nsymbols.)\n\nAn augmented assignment evaluates the target (which, unlike normal\nassignment statements, cannot be an unpacking) and the expression\nlist, performs the binary operation specific to the type of assignment\non the two operands, and assigns the result to the original target.\nThe target is only evaluated once.\n\nAn augmented assignment expression like "x += 1" can be rewritten as\n"x = x + 1" to achieve a similar, but not exactly equal effect. In the\naugmented version, "x" is only evaluated once. Also, when possible,\nthe actual operation is performed *in-place*, meaning that rather than\ncreating a new object and assigning that to the target, the old object\nis modified instead.\n\nUnlike normal assignments, augmented assignments evaluate the left-\nhand side *before* evaluating the right-hand side.  For example, "a[i]\n+= f(x)" first looks-up "a[i]", then it evaluates "f(x)" and performs\nthe addition, and lastly, it writes the result back to "a[i]".\n\nWith the exception of assigning to tuples and multiple targets in a\nsingle statement, the assignment done by augmented assignment\nstatements is handled the same way as normal assignments. Similarly,\nwith the exception of the possible *in-place* behavior, the binary\noperation performed by augmented assignment is the same as the normal\nbinary operations.\n\nFor targets which are attribute references, the same *caveat about\nclass and instance attributes* applies as for regular assignments.\n',
  'atom-identifiers': u'\nIdentifiers (Names)\n*******************\n\nAn identifier occurring as an atom is a name.  See section\n*Identifiers and keywords* for lexical definition and section *Naming\nand binding* for documentation of naming and binding.\n\nWhen the name is bound to an object, evaluation of the atom yields\nthat object. When a name is not bound, an attempt to evaluate it\nraises a "NameError" exception.\n\n**Private name mangling:** When an identifier that textually occurs in\na class definition begins with two or more underscore characters and\ndoes not end in two or more underscores, it is considered a *private\nname* of that class. Private names are transformed to a longer form\nbefore code is generated for them.  The transformation inserts the\nclass name, with leading underscores removed and a single underscore\ninserted, in front of the name.  For example, the identifier "__spam"\noccurring in a class named "Ham" will be transformed to "_Ham__spam".\nThis transformation is independent of the syntactical context in which\nthe identifier is used.  If the transformed name is extremely long\n(longer than 255 characters), implementation defined truncation may\nhappen. If the class name consists only of underscores, no\ntransformation is done.\n',
@@ -18,8 +18,8 @@
  'callable-types': u'\nEmulating callable objects\n**************************\n\nobject.__call__(self[, args...])\n\n   Called when the instance is "called" as a function; if this method\n   is defined, "x(arg1, arg2, ...)" is a shorthand for\n   "x.__call__(arg1, arg2, ...)".\n',
  'calls': u'\nCalls\n*****\n\nA call calls a callable object (e.g., a *function*) with a possibly\nempty series of *arguments*:\n\n   call                 ::= primary "(" [argument_list [","] | comprehension] ")"\n   argument_list        ::= positional_arguments ["," keyword_arguments]\n                       ["," "*" expression] ["," keyword_arguments]\n                       ["," "**" expression]\n                     | keyword_arguments ["," "*" expression]\n                       ["," keyword_arguments] ["," "**" expression]\n                     | "*" expression ["," keyword_arguments] ["," "**" expression]\n                     | "**" expression\n   positional_arguments ::= expression ("," expression)*\n   keyword_arguments    ::= keyword_item ("," keyword_item)*\n   keyword_item         ::= identifier "=" expression\n\nAn optional trailing comma may be present after the positional and\nkeyword arguments but does not affect the semantics.\n\nThe primary must evaluate to a callable object (user-defined\nfunctions, built-in functions, methods of built-in objects, class\nobjects, methods of class instances, and all objects having a\n"__call__()" method are callable).  All argument expressions are\nevaluated before the call is attempted.  Please refer to section\n*Function definitions* for the syntax of formal *parameter* lists.\n\nIf keyword arguments are present, they are first converted to\npositional arguments, as follows.  First, a list of unfilled slots is\ncreated for the formal parameters.  If there are N positional\narguments, they are placed in the first N slots.  Next, for each\nkeyword argument, the identifier is used to determine the\ncorresponding slot (if the identifier is the same as the first formal\nparameter name, the first slot is used, and so on).  If the slot is\nalready filled, a "TypeError" exception is raised. Otherwise, the\nvalue of the argument is placed in the slot, filling it (even if the\nexpression is "None", it fills the slot).  When all arguments have\nbeen processed, the slots that are still unfilled are filled with the\ncorresponding default value from the function definition.  (Default\nvalues are calculated, once, when the function is defined; thus, a\nmutable object such as a list or dictionary used as default value will\nbe shared by all calls that don\'t specify an argument value for the\ncorresponding slot; this should usually be avoided.)  If there are any\nunfilled slots for which no default value is specified, a "TypeError"\nexception is raised.  Otherwise, the list of filled slots is used as\nthe argument list for the call.\n\n**CPython implementation detail:** An implementation may provide\nbuilt-in functions whose positional parameters do not have names, even\nif they are \'named\' for the purpose of documentation, and which\ntherefore cannot be supplied by keyword.  In CPython, this is the case\nfor functions implemented in C that use "PyArg_ParseTuple()" to parse\ntheir arguments.\n\nIf there are more positional arguments than there are formal parameter\nslots, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "*identifier" is present; in this case, that formal\nparameter receives a tuple containing the excess positional arguments\n(or an empty tuple if there were no excess positional arguments).\n\nIf any keyword argument does not correspond to a formal parameter\nname, a "TypeError" exception is raised, unless a formal parameter\nusing the syntax "**identifier" is present; in this case, that formal\nparameter receives a dictionary containing the excess keyword\narguments (using the keywords as keys and the argument values as\ncorresponding values), or a (new) empty dictionary if there were no\nexcess keyword arguments.\n\nIf the syntax "*expression" appears in the function call, "expression"\nmust evaluate to an iterable.  Elements from this iterable are treated\nas if they were additional positional arguments; if there are\npositional arguments *x1*, ..., *xN*, and "expression" evaluates to a\nsequence *y1*, ..., *yM*, this is equivalent to a call with M+N\npositional arguments *x1*, ..., *xN*, *y1*, ..., *yM*.\n\nA consequence of this is that although the "*expression" syntax may\nappear *after* some keyword arguments, it is processed *before* the\nkeyword arguments (and the "**expression" argument, if any -- see\nbelow).  So:\n\n   >>> def f(a, b):\n   ...  print(a, b)\n   ...\n   >>> f(b=1, *(2,))\n   2 1\n   >>> f(a=1, *(2,))\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in ?\n   TypeError: f() got multiple values for keyword argument \'a\'\n   >>> f(1, *(2,))\n   1 2\n\nIt is unusual for both keyword arguments and the "*expression" syntax\nto be used in the same call, so in practice this confusion does not\narise.\n\nIf the syntax "**expression" appears in the function call,\n"expression" must evaluate to a mapping, the contents of which are\ntreated as additional keyword arguments.  In the case of a keyword\nappearing in both "expression" and as an explicit keyword argument, a\n"TypeError" exception is raised.\n\nFormal parameters using the syntax "*identifier" or "**identifier"\ncannot be used as positional argument slots or as keyword argument\nnames.\n\nA call always returns some value, possibly "None", unless it raises an\nexception.  How this value is computed depends on the type of the\ncallable object.\n\nIf it is---\n\na user-defined function:\n   The code block for the function is executed, passing it the\n   argument list.  The first thing the code block will do is bind the\n   formal parameters to the arguments; this is described in section\n   *Function definitions*.  When the code block executes a "return"\n   statement, this specifies the return value of the function call.\n\na built-in function or method:\n   The result is up to the interpreter; see *Built-in Functions* for\n   the descriptions of built-in functions and methods.\n\na class object:\n   A new instance of that class is returned.\n\na class instance method:\n   The corresponding user-defined function is called, with an argument\n   list that is one longer than the argument list of the call: the\n   instance becomes the first argument.\n\na class instance:\n   The class must define a "__call__()" method; the effect is then the\n   same as if that method was called.\n',
  'class': u'\nClass definitions\n*****************\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n   classdef    ::= [decorators] "class" classname [inheritance] ":" suite\n   inheritance ::= "(" [parameter_list] ")"\n   classname   ::= identifier\n\nA class definition is an executable statement.  The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing.  Classes without an inheritance\nlist inherit, by default, from the base class "object"; hence,\n\n   class Foo:\n       pass\n\nis equivalent to\n\n   class Foo(object):\n       pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.)  When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n   @f1(arg)\n   @f2\n   class Foo: pass\n\nis equivalent to\n\n   class Foo: pass\n   Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators.  The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances.  Instance attributes\ncan be set in a method with "self.name = value".  Both class and\ninstance attributes are accessible through the notation ""self.name"",\nand an instance attribute hides a class attribute with the same name\nwhen accessed in this way.  Class attributes can be used as defaults\nfor instance attributes, but using mutable values there can lead to\nunexpected results.  *Descriptors* can be used to create instance\nvariables with different implementation details.\n\nSee also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -\n  Class Decorators\n',
- 'comparisons': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation.  Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n   comparison    ::= or_expr ( comp_operator or_expr )*\n   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n                     | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. They\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of corresponding elements.  This means that to compare equal, each\n  element must compare equal and the two sequences must be of the same\n  type and have the same length.\n\n  If not equal, the sequences are ordered the same as their first\n  differing elements.  For example, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\n\nThe operators "in" and "not in" test for membership.  "x in s"\nevaluates to true if *x* is a member of *s*, and false otherwise.  "x\nnot in s" returns the negation of "x in s".  All built-in sequences\nand set types support this as well as dictionary, for which "in" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception.  (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
- 'compound': u'\nCompound statements\n*******************\n\nCompound statements contain (groups of) other statements; they affect\nor control the execution of those other statements in some way.  In\ngeneral, compound statements span multiple lines, although in simple\nincarnations a whole compound statement may be contained in one line.\n\nThe "if", "while" and "for" statements implement traditional control\nflow constructs.  "try" specifies exception handlers and/or cleanup\ncode for a group of statements, while the "with" statement allows the\nexecution of initialization and finalization code around a block of\ncode.  Function and class definitions are also syntactically compound\nstatements.\n\nA compound statement consists of one or more \'clauses.\'  A clause\nconsists of a header and a \'suite.\'  The clause headers of a\nparticular compound statement are all at the same indentation level.\nEach clause header begins with a uniquely identifying keyword and ends\nwith a colon.  A suite is a group of statements controlled by a\nclause.  A suite can be one or more semicolon-separated simple\nstatements on the same line as the header, following the header\'s\ncolon, or it can be one or more indented statements on subsequent\nlines.  Only the latter form of a suite can contain nested compound\nstatements; the following is illegal, mostly because it wouldn\'t be\nclear to which "if" clause a following "else" clause would belong:\n\n   if test1: if test2: print(x)\n\nAlso note that the semicolon binds tighter than the colon in this\ncontext, so that in the following example, either all or none of the\n"print()" calls are executed:\n\n   if x < y < z: print(x); print(y); print(z)\n\nSummarizing:\n\n   compound_stmt ::= if_stmt\n                     | while_stmt\n                     | for_stmt\n                     | try_stmt\n                     | with_stmt\n                     | funcdef\n                     | classdef\n                     | async_with_stmt\n                     | async_for_stmt\n                     | async_funcdef\n   suite         ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT\n   statement     ::= stmt_list NEWLINE | compound_stmt\n   stmt_list     ::= simple_stmt (";" simple_stmt)* [";"]\n\nNote that statements always end in a "NEWLINE" possibly followed by a\n"DEDENT".  Also note that optional continuation clauses always begin\nwith a keyword that cannot start a statement, thus there are no\nambiguities (the \'dangling "else"\' problem is solved in Python by\nrequiring nested "if" statements to be indented).\n\nThe formatting of the grammar rules in the following sections places\neach clause on a separate line for clarity.\n\n\nThe "if" statement\n==================\n\nThe "if" statement is used for conditional execution:\n\n   if_stmt ::= "if" expression ":" suite\n               ( "elif" expression ":" suite )*\n               ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the "if" statement is executed or evaluated).\nIf all expressions are false, the suite of the "else" clause, if\npresent, is executed.\n\n\nThe "while" statement\n=====================\n\nThe "while" statement is used for repeated execution as long as an\nexpression is true:\n\n   while_stmt ::= "while" expression ":" suite\n                  ["else" ":" suite]\n\nThis repeatedly tests the expression and, if it is true, executes the\nfirst suite; if the expression is false (which may be the first time\nit is tested) the suite of the "else" clause, if present, is executed\nand the loop terminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and goes back\nto testing the expression.\n\n\nThe "for" statement\n===================\n\nThe "for" statement is used to iterate over the elements of a sequence\n(such as a string, tuple or list) or other iterable object:\n\n   for_stmt ::= "for" target_list "in" expression_list ":" suite\n                ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject.  An iterator is created for the result of the\n"expression_list".  The suite is then executed once for each item\nprovided by the iterator, in the order returned by the iterator.  Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted.  When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a "StopIteration" exception),\nthe suite in the "else" clause, if present, is executed, and the loop\nterminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and continues\nwith the next item, or with the "else" clause if there is no next\nitem.\n\nThe for-loop makes assignments to the variables(s) in the target list.\nThis overwrites all previous assignments to those variables including\nthose made in the suite of the for-loop:\n\n   for i in range(10):\n       print(i)\n       i = 5             # this will not affect the for-loop\n                         # because i will be overwritten with the next\n                         # index in the range\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, they will not have been assigned to at\nall by the loop.  Hint: the built-in function "range()" returns an\niterator of integers suitable to emulate the effect of Pascal\'s "for i\n:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".\n\nNote: There is a subtlety when the sequence is being modified by the\n  loop (this can only occur for mutable sequences, i.e. lists).  An\n  internal counter is used to keep track of which item is used next,\n  and this is incremented on each iteration.  When this counter has\n  reached the length of the sequence the loop terminates.  This means\n  that if the suite deletes the current (or a previous) item from the\n  sequence, the next item will be skipped (since it gets the index of\n  the current item which has already been treated).  Likewise, if the\n  suite inserts an item in the sequence before the current item, the\n  current item will be treated again the next time through the loop.\n  This can lead to nasty bugs that can be avoided by making a\n  temporary copy using a slice of the whole sequence, e.g.,\n\n     for x in a[:]:\n         if x < 0: a.remove(x)\n\n\nThe "try" statement\n===================\n\nThe "try" statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" identifier]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe "except" clause(s) specify one or more exception handlers. When no\nexception occurs in the "try" clause, no exception handler is\nexecuted. When an exception occurs in the "try" suite, a search for an\nexception handler is started.  This search inspects the except clauses\nin turn until one is found that matches the exception.  An expression-\nless except clause, if present, must be last; it matches any\nexception.  For an except clause with an expression, that expression\nis evaluated, and the clause matches the exception if the resulting\nobject is "compatible" with the exception.  An object is compatible\nwith an exception if it is the class or a base class of the exception\nobject or a tuple containing an item compatible with the exception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire "try" statement raised\nthe exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the "as" keyword in that except clause, if\npresent, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using "as target", it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the "sys" module and can be accessed via\n"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the\nexception class, the exception instance and a traceback object (see\nsection *The standard type hierarchy*) identifying the point in the\nprogram where the exception occurred.  "sys.exc_info()" values are\nrestored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional "else" clause is executed if and when control flows off\nthe end of the "try" clause. [2] Exceptions in the "else" clause are\nnot handled by the preceding "except" clauses.\n\nIf "finally" is present, it specifies a \'cleanup\' handler.  The "try"\nclause is executed, including any "except" and "else" clauses.  If an\nexception occurs in any of the clauses and is not handled, the\nexception is temporarily saved. The "finally" clause is executed.  If\nthere is a saved exception it is re-raised at the end of the "finally"\nclause.  If the "finally" clause raises another exception, the saved\nexception is set as the context of the new exception. If the "finally"\nclause executes a "return" or "break" statement, the saved exception\nis discarded:\n\n   >>> def f():\n   ...     try:\n   ...         1/0\n   ...     finally:\n   ...         return 42\n   ...\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the "finally" clause.\n\nWhen a "return", "break" or "continue" statement is executed in the\n"try" suite of a "try"..."finally" statement, the "finally" clause is\nalso executed \'on the way out.\' A "continue" statement is illegal in\nthe "finally" clause. (The reason is a problem with the current\nimplementation --- this restriction may be lifted in the future).\n\nThe return value of a function is determined by the last "return"\nstatement executed.  Since the "finally" clause always executes, a\n"return" statement executed in the "finally" clause will always be the\nlast one executed:\n\n   >>> def foo():\n   ...     try:\n   ...         return \'try\'\n   ...     finally:\n   ...         return \'finally\'\n   ...\n   >>> foo()\n   \'finally\'\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the "raise" statement to\ngenerate exceptions may be found in section *The raise statement*.\n\n\nThe "with" statement\n====================\n\nThe "with" statement is used to wrap the execution of a block with\nmethods defined by a context manager (see section *With Statement\nContext Managers*). This allows common "try"..."except"..."finally"\nusage patterns to be encapsulated for convenient reuse.\n\n   with_stmt ::= "with" with_item ("," with_item)* ":" suite\n   with_item ::= expression ["as" target]\n\nThe execution of the "with" statement with one "item" proceeds as\nfollows:\n\n1. The context expression (the expression given in the "with_item")\n   is evaluated to obtain a context manager.\n\n2. The context manager\'s "__exit__()" is loaded for later use.\n\n3. The context manager\'s "__enter__()" method is invoked.\n\n4. If a target was included in the "with" statement, the return\n   value from "__enter__()" is assigned to it.\n\n   Note: The "with" statement guarantees that if the "__enter__()"\n     method returns without an error, then "__exit__()" will always be\n     called. Thus, if an error occurs during the assignment to the\n     target list, it will be treated the same as an error occurring\n     within the suite would be. See step 6 below.\n\n5. The suite is executed.\n\n6. The context manager\'s "__exit__()" method is invoked.  If an\n   exception caused the suite to be exited, its type, value, and\n   traceback are passed as arguments to "__exit__()". Otherwise, three\n   "None" arguments are supplied.\n\n   If the suite was exited due to an exception, and the return value\n   from the "__exit__()" method was false, the exception is reraised.\n   If the return value was true, the exception is suppressed, and\n   execution continues with the statement following the "with"\n   statement.\n\n   If the suite was exited for any reason other than an exception, the\n   return value from "__exit__()" is ignored, and execution proceeds\n   at the normal location for the kind of exit that was taken.\n\nWith more than one item, the context managers are processed as if\nmultiple "with" statements were nested:\n\n   with A() as a, B() as b:\n       suite\n\nis equivalent to\n\n   with A() as a:\n       with B() as b:\n           suite\n\nChanged in version 3.1: Support for multiple context expressions.\n\nSee also: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n\n\nFunction definitions\n====================\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n   funcdef        ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n   decorators     ::= decorator+\n   decorator      ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n   dotted_name    ::= identifier ("." identifier)*\n   parameter_list ::= (defparameter ",")*\n                      | "*" [parameter] ("," defparameter)* ["," "**" parameter]\n                      | "**" parameter\n                      | defparameter [","] )\n   parameter      ::= identifier [":" expression]\n   defparameter   ::= parameter ["=" expression]\n   funcname       ::= identifier\n\nA function definition is an executable statement.  Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function).  This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition.  The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object.  Multiple decorators are applied in\nnested fashion. For example, the following code\n\n   @f1(arg)\n   @f2\n   def func(): pass\n\nis equivalent to\n\n   def func(): pass\n   func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* "="\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted.  If a parameter has a default value, all following\nparameters up until the ""*"" must also have a default value --- this\nis a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call.  This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended.  A way around this is to use\n"None" as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n   def whats_on_the_telly(penguin=None):\n       if penguin is None:\n           penguin = []\n       penguin.append("property of the zoo")\n       return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values.  If the form\n""*identifier"" is present, it is initialized to a tuple receiving any\nexcess positional parameters, defaulting to the empty tuple.  If the\nform ""**identifier"" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after ""*"" or ""*identifier"" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form "": expression"" following\nthe parameter name.  Any parameter may have an annotation even those\nof the form "*identifier" or "**identifier".  Functions may have\n"return" annotation of the form ""-> expression"" after the parameter\nlist.  These annotations can be any valid Python expression and are\nevaluated when the function definition is executed.  Annotations may\nbe evaluated in a different order than they appear in the source code.\nThe presence of annotations does not change the semantics of a\nfunction.  The annotation values are available as values of a\ndictionary keyed by the parameters\' names in the "__annotations__"\nattribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions.  This uses lambda\nexpressions, described in section *Lambdas*.  Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression.  The ""def"" form is actually more powerful since it\nallows the execution of multiple statements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects.  A ""def""\nstatement executed inside a function definition defines a local\nfunction that can be returned or passed around.  Free variables used\nin the nested function can access the local variables of the function\ncontaining the def.  See section *Naming and binding* for details.\n\nSee also: **PEP 3107** - Function Annotations\n\n     The original specification for function annotations.\n\n\nClass definitions\n=================\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n   classdef    ::= [decorators] "class" classname [inheritance] ":" suite\n   inheritance ::= "(" [parameter_list] ")"\n   classname   ::= identifier\n\nA class definition is an executable statement.  The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing.  Classes without an inheritance\nlist inherit, by default, from the base class "object"; hence,\n\n   class Foo:\n       pass\n\nis equivalent to\n\n   class Foo(object):\n       pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.)  When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n   @f1(arg)\n   @f2\n   class Foo: pass\n\nis equivalent to\n\n   class Foo: pass\n   Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators.  The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances.  Instance attributes\ncan be set in a method with "self.name = value".  Both class and\ninstance attributes are accessible through the notation ""self.name"",\nand an instance attribute hides a class attribute with the same name\nwhen accessed in this way.  Class attributes can be used as defaults\nfor instance attributes, but using mutable values there can lead to\nunexpected results.  *Descriptors* can be used to create instance\nvariables with different implementation details.\n\nSee also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -\n  Class Decorators\n\n\nCoroutines\n==========\n\n\nCoroutine function definition\n-----------------------------\n\n   async_funcdef ::= "async" funcdef\n\nExecution of Python coroutines can be suspended and resumed at many\npoints (see *coroutine*.)  "await" expressions, "async for" and "async\nwith" can only be used in their bodies.\n\nFunctions defined with "async def" syntax are always coroutine\nfunctions, even if they do not contain "await" or "async" keywords.\n\nIt is a "SyntaxError" to use "yield" expressions in coroutines.\n\nNew in version 3.5.\n\n\nThe "async for" statement\n-------------------------\n\n   async_for_stmt ::= "async" for_stmt\n\nAn *asynchronous iterable* is able to call asynchronous code in its\n*iter* implementation, and *asynchronous iterator* can call\nasynchronous code in its *next* method.\n\nThe "async for" statement allows convenient iteration over\nasynchronous iterators.\n\nThe following code:\n\n   async for TARGET in ITER:\n       BLOCK\n   else:\n       BLOCK2\n\nIs semantically equivalent to:\n\n   iter = (ITER)\n   iter = await type(iter).__aiter__(iter)\n   running = True\n   while running:\n       try:\n           TARGET = await type(iter).__anext__(iter)\n       except StopAsyncIteration:\n           running = False\n       else:\n           BLOCK\n   else:\n       BLOCK2\n\nSee also "__aiter__()" and "__anext__()" for details.\n\nNew in version 3.5.\n\n\nThe "async with" statement\n--------------------------\n\n   async_with_stmt ::= "async" with_stmt\n\nAn *asynchronous context manager* is a *context manager* that is able\nto suspend execution in its *enter* and *exit* methods.\n\nThe following code:\n\n   async with EXPR as VAR:\n       BLOCK\n\nIs semantically equivalent to:\n\n   mgr = (EXPR)\n   aexit = type(mgr).__aexit__\n   aenter = type(mgr).__aenter__(mgr)\n   exc = True\n\n   VAR = await aenter\n   try:\n       BLOCK\n   except:\n       if not await aexit(mgr, *sys.exc_info()):\n           raise\n   else:\n       await aexit(mgr, None, None, None)\n\nSee also "__aenter__()" and "__aexit__()" for details.\n\nNew in version 3.5.\n\nSee also: **PEP 492** - Coroutines with async and await syntax\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless\n    there is a "finally" clause which happens to raise another\n    exception. That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of\n    an exception or the execution of a "return", "continue", or\n    "break" statement.\n\n[3] A string literal appearing as the first statement in the\n    function body is transformed into the function\'s "__doc__"\n    attribute and therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n    body is transformed into the namespace\'s "__doc__" item and\n    therefore the class\'s *docstring*.\n',
+ 'comparisons': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation.  Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n   comparison    ::= or_expr ( comp_operator or_expr )*\n   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n                     | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. They\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of corresponding elements.  This means that to compare equal, each\n  element must compare equal and the two sequences must be of the same\n  type and have the same length.\n\n  If not equal, the sequences are ordered the same as their first\n  differing elements.  For example, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\n\nThe operators "in" and "not in" test for membership.  "x in s"\nevaluates to true if *x* is a member of *s*, and false otherwise.  "x\nnot in s" returns the negation of "x in s".  All built-in sequences\nand set types support this as well as dictionary, for which "in" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception.  (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
+ 'compound': u'\nCompound statements\n*******************\n\nCompound statements contain (groups of) other statements; they affect\nor control the execution of those other statements in some way.  In\ngeneral, compound statements span multiple lines, although in simple\nincarnations a whole compound statement may be contained in one line.\n\nThe "if", "while" and "for" statements implement traditional control\nflow constructs.  "try" specifies exception handlers and/or cleanup\ncode for a group of statements, while the "with" statement allows the\nexecution of initialization and finalization code around a block of\ncode.  Function and class definitions are also syntactically compound\nstatements.\n\nA compound statement consists of one or more \'clauses.\'  A clause\nconsists of a header and a \'suite.\'  The clause headers of a\nparticular compound statement are all at the same indentation level.\nEach clause header begins with a uniquely identifying keyword and ends\nwith a colon.  A suite is a group of statements controlled by a\nclause.  A suite can be one or more semicolon-separated simple\nstatements on the same line as the header, following the header\'s\ncolon, or it can be one or more indented statements on subsequent\nlines.  Only the latter form of a suite can contain nested compound\nstatements; the following is illegal, mostly because it wouldn\'t be\nclear to which "if" clause a following "else" clause would belong:\n\n   if test1: if test2: print(x)\n\nAlso note that the semicolon binds tighter than the colon in this\ncontext, so that in the following example, either all or none of the\n"print()" calls are executed:\n\n   if x < y < z: print(x); print(y); print(z)\n\nSummarizing:\n\n   compound_stmt ::= if_stmt\n                     | while_stmt\n                     | for_stmt\n                     | try_stmt\n                     | with_stmt\n                     | funcdef\n                     | classdef\n                     | async_with_stmt\n                     | async_for_stmt\n                     | async_funcdef\n   suite         ::= stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT\n   statement     ::= stmt_list NEWLINE | compound_stmt\n   stmt_list     ::= simple_stmt (";" simple_stmt)* [";"]\n\nNote that statements always end in a "NEWLINE" possibly followed by a\n"DEDENT".  Also note that optional continuation clauses always begin\nwith a keyword that cannot start a statement, thus there are no\nambiguities (the \'dangling "else"\' problem is solved in Python by\nrequiring nested "if" statements to be indented).\n\nThe formatting of the grammar rules in the following sections places\neach clause on a separate line for clarity.\n\n\nThe "if" statement\n==================\n\nThe "if" statement is used for conditional execution:\n\n   if_stmt ::= "if" expression ":" suite\n               ( "elif" expression ":" suite )*\n               ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the "if" statement is executed or evaluated).\nIf all expressions are false, the suite of the "else" clause, if\npresent, is executed.\n\n\nThe "while" statement\n=====================\n\nThe "while" statement is used for repeated execution as long as an\nexpression is true:\n\n   while_stmt ::= "while" expression ":" suite\n                  ["else" ":" suite]\n\nThis repeatedly tests the expression and, if it is true, executes the\nfirst suite; if the expression is false (which may be the first time\nit is tested) the suite of the "else" clause, if present, is executed\nand the loop terminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and goes back\nto testing the expression.\n\n\nThe "for" statement\n===================\n\nThe "for" statement is used to iterate over the elements of a sequence\n(such as a string, tuple or list) or other iterable object:\n\n   for_stmt ::= "for" target_list "in" expression_list ":" suite\n                ["else" ":" suite]\n\nThe expression list is evaluated once; it should yield an iterable\nobject.  An iterator is created for the result of the\n"expression_list".  The suite is then executed once for each item\nprovided by the iterator, in the order returned by the iterator.  Each\nitem in turn is assigned to the target list using the standard rules\nfor assignments (see *Assignment statements*), and then the suite is\nexecuted.  When the items are exhausted (which is immediately when the\nsequence is empty or an iterator raises a "StopIteration" exception),\nthe suite in the "else" clause, if present, is executed, and the loop\nterminates.\n\nA "break" statement executed in the first suite terminates the loop\nwithout executing the "else" clause\'s suite.  A "continue" statement\nexecuted in the first suite skips the rest of the suite and continues\nwith the next item, or with the "else" clause if there is no next\nitem.\n\nThe for-loop makes assignments to the variables(s) in the target list.\nThis overwrites all previous assignments to those variables including\nthose made in the suite of the for-loop:\n\n   for i in range(10):\n       print(i)\n       i = 5             # this will not affect the for-loop\n                         # because i will be overwritten with the next\n                         # index in the range\n\nNames in the target list are not deleted when the loop is finished,\nbut if the sequence is empty, they will not have been assigned to at\nall by the loop.  Hint: the built-in function "range()" returns an\niterator of integers suitable to emulate the effect of Pascal\'s "for i\n:= a to b do"; e.g., "list(range(3))" returns the list "[0, 1, 2]".\n\nNote: There is a subtlety when the sequence is being modified by the\n  loop (this can only occur for mutable sequences, i.e. lists).  An\n  internal counter is used to keep track of which item is used next,\n  and this is incremented on each iteration.  When this counter has\n  reached the length of the sequence the loop terminates.  This means\n  that if the suite deletes the current (or a previous) item from the\n  sequence, the next item will be skipped (since it gets the index of\n  the current item which has already been treated).  Likewise, if the\n  suite inserts an item in the sequence before the current item, the\n  current item will be treated again the next time through the loop.\n  This can lead to nasty bugs that can be avoided by making a\n  temporary copy using a slice of the whole sequence, e.g.,\n\n     for x in a[:]:\n         if x < 0: a.remove(x)\n\n\nThe "try" statement\n===================\n\nThe "try" statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" identifier]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe "except" clause(s) specify one or more exception handlers. When no\nexception occurs in the "try" clause, no exception handler is\nexecuted. When an exception occurs in the "try" suite, a search for an\nexception handler is started.  This search inspects the except clauses\nin turn until one is found that matches the exception.  An expression-\nless except clause, if present, must be last; it matches any\nexception.  For an except clause with an expression, that expression\nis evaluated, and the clause matches the exception if the resulting\nobject is "compatible" with the exception.  An object is compatible\nwith an exception if it is the class or a base class of the exception\nobject or a tuple containing an item compatible with the exception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire "try" statement raised\nthe exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the "as" keyword in that except clause, if\npresent, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using "as target", it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the "sys" module and can be accessed via\n"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the\nexception class, the exception instance and a traceback object (see\nsection *The standard type hierarchy*) identifying the point in the\nprogram where the exception occurred.  "sys.exc_info()" values are\nrestored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional "else" clause is executed if and when control flows off\nthe end of the "try" clause. [2] Exceptions in the "else" clause are\nnot handled by the preceding "except" clauses.\n\nIf "finally" is present, it specifies a \'cleanup\' handler.  The "try"\nclause is executed, including any "except" and "else" clauses.  If an\nexception occurs in any of the clauses and is not handled, the\nexception is temporarily saved. The "finally" clause is executed.  If\nthere is a saved exception it is re-raised at the end of the "finally"\nclause.  If the "finally" clause raises another exception, the saved\nexception is set as the context of the new exception. If the "finally"\nclause executes a "return" or "break" statement, the saved exception\nis discarded:\n\n   >>> def f():\n   ...     try:\n   ...         1/0\n   ...     finally:\n   ...         return 42\n   ...\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the "finally" clause.\n\nWhen a "return", "break" or "continue" statement is executed in the\n"try" suite of a "try"..."finally" statement, the "finally" clause is\nalso executed \'on the way out.\' A "continue" statement is illegal in\nthe "finally" clause. (The reason is a problem with the current\nimplementation --- this restriction may be lifted in the future).\n\nThe return value of a function is determined by the last "return"\nstatement executed.  Since the "finally" clause always executes, a\n"return" statement executed in the "finally" clause will always be the\nlast one executed:\n\n   >>> def foo():\n   ...     try:\n   ...         return \'try\'\n   ...     finally:\n   ...         return \'finally\'\n   ...\n   >>> foo()\n   \'finally\'\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the "raise" statement to\ngenerate exceptions may be found in section *The raise statement*.\n\n\nThe "with" statement\n====================\n\nThe "with" statement is used to wrap the execution of a block with\nmethods defined by a context manager (see section *With Statement\nContext Managers*). This allows common "try"..."except"..."finally"\nusage patterns to be encapsulated for convenient reuse.\n\n   with_stmt ::= "with" with_item ("," with_item)* ":" suite\n   with_item ::= expression ["as" target]\n\nThe execution of the "with" statement with one "item" proceeds as\nfollows:\n\n1. The context expression (the expression given in the "with_item")\n   is evaluated to obtain a context manager.\n\n2. The context manager\'s "__exit__()" is loaded for later use.\n\n3. The context manager\'s "__enter__()" method is invoked.\n\n4. If a target was included in the "with" statement, the return\n   value from "__enter__()" is assigned to it.\n\n   Note: The "with" statement guarantees that if the "__enter__()"\n     method returns without an error, then "__exit__()" will always be\n     called. Thus, if an error occurs during the assignment to the\n     target list, it will be treated the same as an error occurring\n     within the suite would be. See step 6 below.\n\n5. The suite is executed.\n\n6. The context manager\'s "__exit__()" method is invoked.  If an\n   exception caused the suite to be exited, its type, value, and\n   traceback are passed as arguments to "__exit__()". Otherwise, three\n   "None" arguments are supplied.\n\n   If the suite was exited due to an exception, and the return value\n   from the "__exit__()" method was false, the exception is reraised.\n   If the return value was true, the exception is suppressed, and\n   execution continues with the statement following the "with"\n   statement.\n\n   If the suite was exited for any reason other than an exception, the\n   return value from "__exit__()" is ignored, and execution proceeds\n   at the normal location for the kind of exit that was taken.\n\nWith more than one item, the context managers are processed as if\nmultiple "with" statements were nested:\n\n   with A() as a, B() as b:\n       suite\n\nis equivalent to\n\n   with A() as a:\n       with B() as b:\n           suite\n\nChanged in version 3.1: Support for multiple context expressions.\n\nSee also: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n\n\nFunction definitions\n====================\n\nA function definition defines a user-defined function object (see\nsection *The standard type hierarchy*):\n\n   funcdef        ::= [decorators] "def" funcname "(" [parameter_list] ")" ["->" expression] ":" suite\n   decorators     ::= decorator+\n   decorator      ::= "@" dotted_name ["(" [parameter_list [","]] ")"] NEWLINE\n   dotted_name    ::= identifier ("." identifier)*\n   parameter_list ::= (defparameter ",")*\n                      | "*" [parameter] ("," defparameter)* ["," "**" parameter]\n                      | "**" parameter\n                      | defparameter [","] )\n   parameter      ::= identifier [":" expression]\n   defparameter   ::= parameter ["=" expression]\n   funcname       ::= identifier\n\nA function definition is an executable statement.  Its execution binds\nthe function name in the current local namespace to a function object\n(a wrapper around the executable code for the function).  This\nfunction object contains a reference to the current global namespace\nas the global namespace to be used when the function is called.\n\nThe function definition does not execute the function body; this gets\nexecuted only when the function is called. [3]\n\nA function definition may be wrapped by one or more *decorator*\nexpressions. Decorator expressions are evaluated when the function is\ndefined, in the scope that contains the function definition.  The\nresult must be a callable, which is invoked with the function object\nas the only argument. The returned value is bound to the function name\ninstead of the function object.  Multiple decorators are applied in\nnested fashion. For example, the following code\n\n   @f1(arg)\n   @f2\n   def func(): pass\n\nis equivalent to\n\n   def func(): pass\n   func = f1(arg)(f2(func))\n\nWhen one or more *parameters* have the form *parameter* "="\n*expression*, the function is said to have "default parameter values."\nFor a parameter with a default value, the corresponding *argument* may\nbe omitted from a call, in which case the parameter\'s default value is\nsubstituted.  If a parameter has a default value, all following\nparameters up until the ""*"" must also have a default value --- this\nis a syntactic restriction that is not expressed by the grammar.\n\n**Default parameter values are evaluated from left to right when the\nfunction definition is executed.** This means that the expression is\nevaluated once, when the function is defined, and that the same "pre-\ncomputed" value is used for each call.  This is especially important\nto understand when a default parameter is a mutable object, such as a\nlist or a dictionary: if the function modifies the object (e.g. by\nappending an item to a list), the default value is in effect modified.\nThis is generally not what was intended.  A way around this is to use\n"None" as the default, and explicitly test for it in the body of the\nfunction, e.g.:\n\n   def whats_on_the_telly(penguin=None):\n       if penguin is None:\n           penguin = []\n       penguin.append("property of the zoo")\n       return penguin\n\nFunction call semantics are described in more detail in section\n*Calls*. A function call always assigns values to all parameters\nmentioned in the parameter list, either from position arguments, from\nkeyword arguments, or from default values.  If the form\n""*identifier"" is present, it is initialized to a tuple receiving any\nexcess positional parameters, defaulting to the empty tuple.  If the\nform ""**identifier"" is present, it is initialized to a new\ndictionary receiving any excess keyword arguments, defaulting to a new\nempty dictionary. Parameters after ""*"" or ""*identifier"" are\nkeyword-only parameters and may only be passed used keyword arguments.\n\nParameters may have annotations of the form "": expression"" following\nthe parameter name.  Any parameter may have an annotation even those\nof the form "*identifier" or "**identifier".  Functions may have\n"return" annotation of the form ""-> expression"" after the parameter\nlist.  These annotations can be any valid Python expression and are\nevaluated when the function definition is executed.  Annotations may\nbe evaluated in a different order than they appear in the source code.\nThe presence of annotations does not change the semantics of a\nfunction.  The annotation values are available as values of a\ndictionary keyed by the parameters\' names in the "__annotations__"\nattribute of the function object.\n\nIt is also possible to create anonymous functions (functions not bound\nto a name), for immediate use in expressions.  This uses lambda\nexpressions, described in section *Lambdas*.  Note that the lambda\nexpression is merely a shorthand for a simplified function definition;\na function defined in a ""def"" statement can be passed around or\nassigned to another name just like a function defined by a lambda\nexpression.  The ""def"" form is actually more powerful since it\nallows the execution of multiple statements and annotations.\n\n**Programmer\'s note:** Functions are first-class objects.  A ""def""\nstatement executed inside a function definition defines a local\nfunction that can be returned or passed around.  Free variables used\nin the nested function can access the local variables of the function\ncontaining the def.  See section *Naming and binding* for details.\n\nSee also: **PEP 3107** - Function Annotations\n\n     The original specification for function annotations.\n\n\nClass definitions\n=================\n\nA class definition defines a class object (see section *The standard\ntype hierarchy*):\n\n   classdef    ::= [decorators] "class" classname [inheritance] ":" suite\n   inheritance ::= "(" [parameter_list] ")"\n   classname   ::= identifier\n\nA class definition is an executable statement.  The inheritance list\nusually gives a list of base classes (see *Customizing class creation*\nfor more advanced uses), so each item in the list should evaluate to a\nclass object which allows subclassing.  Classes without an inheritance\nlist inherit, by default, from the base class "object"; hence,\n\n   class Foo:\n       pass\n\nis equivalent to\n\n   class Foo(object):\n       pass\n\nThe class\'s suite is then executed in a new execution frame (see\n*Naming and binding*), using a newly created local namespace and the\noriginal global namespace. (Usually, the suite contains mostly\nfunction definitions.)  When the class\'s suite finishes execution, its\nexecution frame is discarded but its local namespace is saved. [4] A\nclass object is then created using the inheritance list for the base\nclasses and the saved local namespace for the attribute dictionary.\nThe class name is bound to this class object in the original local\nnamespace.\n\nClass creation can be customized heavily using *metaclasses*.\n\nClasses can also be decorated: just like when decorating functions,\n\n   @f1(arg)\n   @f2\n   class Foo: pass\n\nis equivalent to\n\n   class Foo: pass\n   Foo = f1(arg)(f2(Foo))\n\nThe evaluation rules for the decorator expressions are the same as for\nfunction decorators.  The result must be a class object, which is then\nbound to the class name.\n\n**Programmer\'s note:** Variables defined in the class definition are\nclass attributes; they are shared by instances.  Instance attributes\ncan be set in a method with "self.name = value".  Both class and\ninstance attributes are accessible through the notation ""self.name"",\nand an instance attribute hides a class attribute with the same name\nwhen accessed in this way.  Class attributes can be used as defaults\nfor instance attributes, but using mutable values there can lead to\nunexpected results.  *Descriptors* can be used to create instance\nvariables with different implementation details.\n\nSee also: **PEP 3115** - Metaclasses in Python 3 **PEP 3129** -\n  Class Decorators\n\n\nCoroutines\n==========\n\nNew in version 3.5.\n\n\nCoroutine function definition\n-----------------------------\n\n   async_funcdef ::= "async" funcdef\n\nExecution of Python coroutines can be suspended and resumed at many\npoints (see *coroutine*).  In the body of a coroutine, any "await" and\n"async" identifiers become reserved keywords; "await" expressions,\n"async for" and "async with" can only be used in coroutine bodies.\nHowever, to simplify the parser, these keywords cannot be used on the\nsame line as a function or coroutine ("def" statement) header.\n\nFunctions defined with "async def" syntax are always coroutine\nfunctions, even if they do not contain "await" or "async" keywords.\n\nIt is a "SyntaxError" to use "yield" expressions in "async def"\ncoroutines.\n\nAn example of a coroutine function:\n\n   async def func(param1, param2):\n       do_stuff()\n       await some_coroutine()\n\n\nThe "async for" statement\n-------------------------\n\n   async_for_stmt ::= "async" for_stmt\n\nAn *asynchronous iterable* is able to call asynchronous code in its\n*iter* implementation, and *asynchronous iterator* can call\nasynchronous code in its *next* method.\n\nThe "async for" statement allows convenient iteration over\nasynchronous iterators.\n\nThe following code:\n\n   async for TARGET in ITER:\n       BLOCK\n   else:\n       BLOCK2\n\nIs semantically equivalent to:\n\n   iter = (ITER)\n   iter = await type(iter).__aiter__(iter)\n   running = True\n   while running:\n       try:\n           TARGET = await type(iter).__anext__(iter)\n       except StopAsyncIteration:\n           running = False\n       else:\n           BLOCK\n   else:\n       BLOCK2\n\nSee also "__aiter__()" and "__anext__()" for details.\n\nIt is a "SyntaxError" to use "async for" statement outside of an\n"async def" function.\n\n\nThe "async with" statement\n--------------------------\n\n   async_with_stmt ::= "async" with_stmt\n\nAn *asynchronous context manager* is a *context manager* that is able\nto suspend execution in its *enter* and *exit* methods.\n\nThe following code:\n\n   async with EXPR as VAR:\n       BLOCK\n\nIs semantically equivalent to:\n\n   mgr = (EXPR)\n   aexit = type(mgr).__aexit__\n   aenter = type(mgr).__aenter__(mgr)\n   exc = True\n\n   VAR = await aenter\n   try:\n       BLOCK\n   except:\n       if not await aexit(mgr, *sys.exc_info()):\n           raise\n   else:\n       await aexit(mgr, None, None, None)\n\nSee also "__aenter__()" and "__aexit__()" for details.\n\nIt is a "SyntaxError" to use "async with" statement outside of an\n"async def" function.\n\nSee also: **PEP 492** - Coroutines with async and await syntax\n\n-[ Footnotes ]-\n\n[1] The exception is propagated to the invocation stack unless\n    there is a "finally" clause which happens to raise another\n    exception. That new exception causes the old one to be lost.\n\n[2] Currently, control "flows off the end" except in the case of\n    an exception or the execution of a "return", "continue", or\n    "break" statement.\n\n[3] A string literal appearing as the first statement in the\n    function body is transformed into the function\'s "__doc__"\n    attribute and therefore the function\'s *docstring*.\n\n[4] A string literal appearing as the first statement in the class\n    body is transformed into the namespace\'s "__doc__" item and\n    therefore the class\'s *docstring*.\n',
  'context-managers': u'\nWith Statement Context Managers\n*******************************\n\nA *context manager* is an object that defines the runtime context to\nbe established when executing a "with" statement. The context manager\nhandles the entry into, and the exit from, the desired runtime context\nfor the execution of the block of code.  Context managers are normally\ninvoked using the "with" statement (described in section *The with\nstatement*), but can also be used by directly invoking their methods.\n\nTypical uses of context managers include saving and restoring various\nkinds of global state, locking and unlocking resources, closing opened\nfiles, etc.\n\nFor more information on context managers, see *Context Manager Types*.\n\nobject.__enter__(self)\n\n   Enter the runtime context related to this object. The "with"\n   statement will bind this method\'s return value to the target(s)\n   specified in the "as" clause of the statement, if any.\n\nobject.__exit__(self, exc_type, exc_value, traceback)\n\n   Exit the runtime context related to this object. The parameters\n   describe the exception that caused the context to be exited. If the\n   context was exited without an exception, all three arguments will\n   be "None".\n\n   If an exception is supplied, and the method wishes to suppress the\n   exception (i.e., prevent it from being propagated), it should\n   return a true value. Otherwise, the exception will be processed\n   normally upon exit from this method.\n\n   Note that "__exit__()" methods should not reraise the passed-in\n   exception; this is the caller\'s responsibility.\n\nSee also: **PEP 0343** - The "with" statement\n\n     The specification, background, and examples for the Python "with"\n     statement.\n',
  'continue': u'\nThe "continue" statement\n************************\n\n   continue_stmt ::= "continue"\n\n"continue" may only occur syntactically nested in a "for" or "while"\nloop, but not nested in a function or class definition or "finally"\nclause within that loop.  It continues with the next cycle of the\nnearest enclosing loop.\n\nWhen "continue" passes control out of a "try" statement with a\n"finally" clause, that "finally" clause is executed before really\nstarting the next loop cycle.\n',
  'conversions': u'\nArithmetic conversions\n**********************\n\nWhen a description of an arithmetic operator below uses the phrase\n"the numeric arguments are converted to a common type," this means\nthat the operator implementation for built-in types works as follows:\n\n* If either argument is a complex number, the other is converted to\n  complex;\n\n* otherwise, if either argument is a floating point number, the\n  other is converted to floating point;\n\n* otherwise, both must be integers and no conversion is necessary.\n\nSome additional rules apply for certain operators (e.g., a string as a\nleft argument to the \'%\' operator).  Extensions must define their own\nconversion behavior.\n',
@@ -42,7 +42,7 @@
  'if': u'\nThe "if" statement\n******************\n\nThe "if" statement is used for conditional execution:\n\n   if_stmt ::= "if" expression ":" suite\n               ( "elif" expression ":" suite )*\n               ["else" ":" suite]\n\nIt selects exactly one of the suites by evaluating the expressions one\nby one until one is found to be true (see section *Boolean operations*\nfor the definition of true and false); then that suite is executed\n(and no other part of the "if" statement is executed or evaluated).\nIf all expressions are false, the suite of the "else" clause, if\npresent, is executed.\n',
  'imaginary': u'\nImaginary literals\n******************\n\nImaginary literals are described by the following lexical definitions:\n\n   imagnumber ::= (floatnumber | intpart) ("j" | "J")\n\nAn imaginary literal yields a complex number with a real part of 0.0.\nComplex numbers are represented as a pair of floating point numbers\nand have the same restrictions on their range.  To create a complex\nnumber with a nonzero real part, add a floating point number to it,\ne.g., "(3+4j)".  Some examples of imaginary literals:\n\n   3.14j   10.j    10j     .001j   1e100j  3.14e-10j\n',
  'import': u'\nThe "import" statement\n**********************\n\n   import_stmt     ::= "import" module ["as" name] ( "," module ["as" name] )*\n                   | "from" relative_module "import" identifier ["as" name]\n                   ( "," identifier ["as" name] )*\n                   | "from" relative_module "import" "(" identifier ["as" name]\n                   ( "," identifier ["as" name] )* [","] ")"\n                   | "from" module "import" "*"\n   module          ::= (identifier ".")* identifier\n   relative_module ::= "."* module | "."+\n   name            ::= identifier\n\nThe basic import statement (no "from" clause) is executed in two\nsteps:\n\n1. find a module, loading and initializing it if necessary\n\n2. define a name or names in the local namespace for the scope\n   where the "import" statement occurs.\n\nWhen the statement contains multiple clauses (separated by commas) the\ntwo steps are carried out separately for each clause, just as though\nthe clauses had been separated out into individiual import statements.\n\nThe details of the first step, finding and loading modules are\ndescribed in greater detail in the section on the *import system*,\nwhich also describes the various types of packages and modules that\ncan be imported, as well as all the hooks that can be used to\ncustomize the import system. Note that failures in this step may\nindicate either that the module could not be located, *or* that an\nerror occurred while initializing the module, which includes execution\nof the module\'s code.\n\nIf the requested module is retrieved successfully, it will be made\navailable in the local namespace in one of three ways:\n\n* If the module name is followed by "as", then the name following\n  "as" is bound directly to the imported module.\n\n* If no other name is specified, and the module being imported is a\n  top level module, the module\'s name is bound in the local namespace\n  as a reference to the imported module\n\n* If the module being imported is *not* a top level module, then the\n  name of the top level package that contains the module is bound in\n  the local namespace as a reference to the top level package. The\n  imported module must be accessed using its full qualified name\n  rather than directly\n\nThe "from" form uses a slightly more complex process:\n\n1. find the module specified in the "from" clause, loading and\n   initializing it if necessary;\n\n2. for each of the identifiers specified in the "import" clauses:\n\n   1. check if the imported module has an attribute by that name\n\n   2. if not, attempt to import a submodule with that name and then\n      check the imported module again for that attribute\n\n   3. if the attribute is not found, "ImportError" is raised.\n\n   4. otherwise, a reference to that value is stored in the local\n      namespace, using the name in the "as" clause if it is present,\n      otherwise using the attribute name\n\nExamples:\n\n   import foo                 # foo imported and bound locally\n   import foo.bar.baz         # foo.bar.baz imported, foo bound locally\n   import foo.bar.baz as fbb  # foo.bar.baz imported and bound as fbb\n   from foo.bar import baz    # foo.bar.baz imported and bound as baz\n   from foo import attr       # foo imported and foo.attr bound as attr\n\nIf the list of identifiers is replaced by a star ("\'*\'"), all public\nnames defined in the module are bound in the local namespace for the\nscope where the "import" statement occurs.\n\nThe *public names* defined by a module are determined by checking the\nmodule\'s namespace for a variable named "__all__"; if defined, it must\nbe a sequence of strings which are names defined or imported by that\nmodule.  The names given in "__all__" are all considered public and\nare required to exist.  If "__all__" is not defined, the set of public\nnames includes all names found in the module\'s namespace which do not\nbegin with an underscore character ("\'_\'").  "__all__" should contain\nthe entire public API. It is intended to avoid accidentally exporting\nitems that are not part of the API (such as library modules which were\nimported and used within the module).\n\nThe wild card form of import --- "from module import *" --- is only\nallowed at the module level.  Attempting to use it in class or\nfunction definitions will raise a "SyntaxError".\n\nWhen specifying what module to import you do not have to specify the\nabsolute name of the module. When a module or package is contained\nwithin another package it is possible to make a relative import within\nthe same top package without having to mention the package name. By\nusing leading dots in the specified module or package after "from" you\ncan specify how high to traverse up the current package hierarchy\nwithout specifying exact names. One leading dot means the current\npackage where the module making the import exists. Two dots means up\none package level. Three dots is up two levels, etc. So if you execute\n"from . import mod" from a module in the "pkg" package then you will\nend up importing "pkg.mod". If you execute "from ..subpkg2 import mod"\nfrom within "pkg.subpkg1" you will import "pkg.subpkg2.mod". The\nspecification for relative imports is contained within **PEP 328**.\n\n"importlib.import_module()" is provided to support applications that\ndetermine dynamically the modules to be loaded.\n\n\nFuture statements\n=================\n\nA *future statement* is a directive to the compiler that a particular\nmodule should be compiled using syntax or semantics that will be\navailable in a specified future release of Python where the feature\nbecomes standard.\n\nThe future statement is intended to ease migration to future versions\nof Python that introduce incompatible changes to the language.  It\nallows use of the new features on a per-module basis before the\nrelease in which the feature becomes standard.\n\n   future_statement ::= "from" "__future__" "import" feature ["as" name]\n                        ("," feature ["as" name])*\n                        | "from" "__future__" "import" "(" feature ["as" name]\n                        ("," feature ["as" name])* [","] ")"\n   feature          ::= identifier\n   name             ::= identifier\n\nA future statement must appear near the top of the module.  The only\nlines that can appear before a future statement are:\n\n* the module docstring (if any),\n\n* comments,\n\n* blank lines, and\n\n* other future statements.\n\nThe features recognized by Python 3.0 are "absolute_import",\n"division", "generators", "unicode_literals", "print_function",\n"nested_scopes" and "with_statement".  They are all redundant because\nthey are always enabled, and only kept for backwards compatibility.\n\nA future statement is recognized and treated specially at compile\ntime: Changes to the semantics of core constructs are often\nimplemented by generating different code.  It may even be the case\nthat a new feature introduces new incompatible syntax (such as a new\nreserved word), in which case the compiler may need to parse the\nmodule differently.  Such decisions cannot be pushed off until\nruntime.\n\nFor any given release, the compiler knows which feature names have\nbeen defined, and raises a compile-time error if a future statement\ncontains a feature not known to it.\n\nThe direct runtime semantics are the same as for any import statement:\nthere is a standard module "__future__", described later, and it will\nbe imported in the usual way at the time the future statement is\nexecuted.\n\nThe interesting runtime semantics depend on the specific feature\nenabled by the future statement.\n\nNote that there is nothing special about the statement:\n\n   import __future__ [as name]\n\nThat is not a future statement; it\'s an ordinary import statement with\nno special semantics or syntax restrictions.\n\nCode compiled by calls to the built-in functions "exec()" and\n"compile()" that occur in a module "M" containing a future statement\nwill, by default, use the new syntax or semantics associated with the\nfuture statement.  This can be controlled by optional arguments to\n"compile()" --- see the documentation of that function for details.\n\nA future statement typed at an interactive interpreter prompt will\ntake effect for the rest of the interpreter session.  If an\ninterpreter is started with the *-i* option, is passed a script name\nto execute, and the script includes a future statement, it will be in\neffect in the interactive session started after the script is\nexecuted.\n\nSee also: **PEP 236** - Back to the __future__\n\n     The original proposal for the __future__ mechanism.\n',
- 'in': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation.  Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n   comparison    ::= or_expr ( comp_operator or_expr )*\n   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n                     | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. They\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of corresponding elements.  This means that to compare equal, each\n  element must compare equal and the two sequences must be of the same\n  type and have the same length.\n\n  If not equal, the sequences are ordered the same as their first\n  differing elements.  For example, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and {2,3} are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\n\nThe operators "in" and "not in" test for membership.  "x in s"\nevaluates to true if *x* is a member of *s*, and false otherwise.  "x\nnot in s" returns the negation of "x in s".  All built-in sequences\nand set types support this as well as dictionary, for which "in" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception.  (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
+ 'in': u'\nComparisons\n***********\n\nUnlike C, all comparison operations in Python have the same priority,\nwhich is lower than that of any arithmetic, shifting or bitwise\noperation.  Also unlike C, expressions like "a < b < c" have the\ninterpretation that is conventional in mathematics:\n\n   comparison    ::= or_expr ( comp_operator or_expr )*\n   comp_operator ::= "<" | ">" | "==" | ">=" | "<=" | "!="\n                     | "is" ["not"] | ["not"] "in"\n\nComparisons yield boolean values: "True" or "False".\n\nComparisons can be chained arbitrarily, e.g., "x < y <= z" is\nequivalent to "x < y and y <= z", except that "y" is evaluated only\nonce (but in both cases "z" is not evaluated at all when "x < y" is\nfound to be false).\n\nFormally, if *a*, *b*, *c*, ..., *y*, *z* are expressions and *op1*,\n*op2*, ..., *opN* are comparison operators, then "a op1 b op2 c ... y\nopN z" is equivalent to "a op1 b and b op2 c and ... y opN z", except\nthat each expression is evaluated at most once.\n\nNote that "a op1 b op2 c" doesn\'t imply any kind of comparison between\n*a* and *c*, so that, e.g., "x < y > z" is perfectly legal (though\nperhaps not pretty).\n\nThe operators "<", ">", "==", ">=", "<=", and "!=" compare the values\nof two objects.  The objects need not have the same type. If both are\nnumbers, they are converted to a common type.  Otherwise, the "==" and\n"!=" operators *always* consider objects of different types to be\nunequal, while the "<", ">", ">=" and "<=" operators raise a\n"TypeError" when comparing objects of different types that do not\nimplement these operators for the given pair of types.  You can\ncontrol comparison behavior of objects of non-built-in types by\ndefining rich comparison methods like "__gt__()", described in section\n*Basic customization*.\n\nComparison of objects of the same type depends on the type:\n\n* Numbers are compared arithmetically.\n\n* The values "float(\'NaN\')" and "Decimal(\'NaN\')" are special. They\n  are identical to themselves, "x is x" but are not equal to\n  themselves, "x != x".  Additionally, comparing any value to a\n  not-a-number value will return "False".  For example, both "3 <\n  float(\'NaN\')" and "float(\'NaN\') < 3" will return "False".\n\n* Bytes objects are compared lexicographically using the numeric\n  values of their elements.\n\n* Strings are compared lexicographically using the numeric\n  equivalents (the result of the built-in function "ord()") of their\n  characters. [3] String and bytes object can\'t be compared!\n\n* Tuples and lists are compared lexicographically using comparison\n  of corresponding elements.  This means that to compare equal, each\n  element must compare equal and the two sequences must be of the same\n  type and have the same length.\n\n  If not equal, the sequences are ordered the same as their first\n  differing elements.  For example, "[1,2,x] <= [1,2,y]" has the same\n  value as "x <= y".  If the corresponding element does not exist, the\n  shorter sequence is ordered first (for example, "[1,2] < [1,2,3]").\n\n* Mappings (dictionaries) compare equal if and only if they have the\n  same "(key, value)" pairs. Order comparisons "(\'<\', \'<=\', \'>=\',\n  \'>\')" raise "TypeError".\n\n* Sets and frozensets define comparison operators to mean subset and\n  superset tests.  Those relations do not define total orderings (the\n  two sets "{1,2}" and "{2,3}" are not equal, nor subsets of one\n  another, nor supersets of one another).  Accordingly, sets are not\n  appropriate arguments for functions which depend on total ordering.\n  For example, "min()", "max()", and "sorted()" produce undefined\n  results given a list of sets as inputs.\n\n* Most other objects of built-in types compare unequal unless they\n  are the same object; the choice whether one object is considered\n  smaller or larger than another one is made arbitrarily but\n  consistently within one execution of a program.\n\nComparison of objects of differing types depends on whether either of\nthe types provide explicit support for the comparison.  Most numeric\ntypes can be compared with one another.  When cross-type comparison is\nnot supported, the comparison method returns "NotImplemented".\n\nThe operators "in" and "not in" test for membership.  "x in s"\nevaluates to true if *x* is a member of *s*, and false otherwise.  "x\nnot in s" returns the negation of "x in s".  All built-in sequences\nand set types support this as well as dictionary, for which "in" tests\nwhether the dictionary has a given key. For container types such as\nlist, tuple, set, frozenset, dict, or collections.deque, the\nexpression "x in y" is equivalent to "any(x is e or x == e for e in\ny)".\n\nFor the string and bytes types, "x in y" is true if and only if *x* is\na substring of *y*.  An equivalent test is "y.find(x) != -1".  Empty\nstrings are always considered to be a substring of any other string,\nso """ in "abc"" will return "True".\n\nFor user-defined classes which define the "__contains__()" method, "x\nin y" is true if and only if "y.__contains__(x)" is true.\n\nFor user-defined classes which do not define "__contains__()" but do\ndefine "__iter__()", "x in y" is true if some value "z" with "x == z"\nis produced while iterating over "y".  If an exception is raised\nduring the iteration, it is as if "in" raised that exception.\n\nLastly, the old-style iteration protocol is tried: if a class defines\n"__getitem__()", "x in y" is true if and only if there is a non-\nnegative integer index *i* such that "x == y[i]", and all lower\ninteger indices do not raise "IndexError" exception.  (If any other\nexception is raised, it is as if "in" raised that exception).\n\nThe operator "not in" is defined to have the inverse true value of\n"in".\n\nThe operators "is" and "is not" test for object identity: "x is y" is\ntrue if and only if *x* and *y* are the same object.  "x is not y"\nyields the inverse truth value. [4]\n',
  'integers': u'\nInteger literals\n****************\n\nInteger literals are described by the following lexical definitions:\n\n   integer        ::= decimalinteger | octinteger | hexinteger | bininteger\n   decimalinteger ::= nonzerodigit digit* | "0"+\n   nonzerodigit   ::= "1"..."9"\n   digit          ::= "0"..."9"\n   octinteger     ::= "0" ("o" | "O") octdigit+\n   hexinteger     ::= "0" ("x" | "X") hexdigit+\n   bininteger     ::= "0" ("b" | "B") bindigit+\n   octdigit       ::= "0"..."7"\n   hexdigit       ::= digit | "a"..."f" | "A"..."F"\n   bindigit       ::= "0" | "1"\n\nThere is no limit for the length of integer literals apart from what\ncan be stored in available memory.\n\nNote that leading zeros in a non-zero decimal number are not allowed.\nThis is for disambiguation with C-style octal literals, which Python\nused before version 3.0.\n\nSome examples of integer literals:\n\n   7     2147483647                        0o177    0b100110111\n   3     79228162514264337593543950336     0o377    0xdeadbeef\n',
  'lambda': u'\nLambdas\n*******\n\n   lambda_expr        ::= "lambda" [parameter_list]: expression\n   lambda_expr_nocond ::= "lambda" [parameter_list]: expression_nocond\n\nLambda expressions (sometimes called lambda forms) are used to create\nanonymous functions. The expression "lambda arguments: expression"\nyields a function object.  The unnamed object behaves like a function\nobject defined with\n\n   def <lambda>(arguments):\n       return expression\n\nSee section *Function definitions* for the syntax of parameter lists.\nNote that functions created with lambda expressions cannot contain\nstatements or annotations.\n',
  'lists': u'\nList displays\n*************\n\nA list display is a possibly empty series of expressions enclosed in\nsquare brackets:\n\n   list_display ::= "[" [expression_list | comprehension] "]"\n\nA list display yields a new list object, the contents being specified\nby either a list of expressions or a comprehension.  When a comma-\nseparated list of expressions is supplied, its elements are evaluated\nfrom left to right and placed into the list object in that order.\nWhen a comprehension is supplied, the list is constructed from the\nelements resulting from the comprehension.\n',
@@ -66,9 +66,9 @@
  'subscriptions': u'\nSubscriptions\n*************\n\nA subscription selects an item of a sequence (string, tuple or list)\nor mapping (dictionary) object:\n\n   subscription ::= primary "[" expression_list "]"\n\nThe primary must evaluate to an object that supports subscription\n(lists or dictionaries for example).  User-defined objects can support\nsubscription by defining a "__getitem__()" method.\n\nFor built-in objects, there are two types of objects that support\nsubscription:\n\nIf the primary is a mapping, the expression list must evaluate to an\nobject whose value is one of the keys of the mapping, and the\nsubscription selects the value in the mapping that corresponds to that\nkey.  (The expression list is a tuple except if it has exactly one\nitem.)\n\nIf the primary is a sequence, the expression (list) must evaluate to\nan integer or a slice (as discussed in the following section).\n\nThe formal syntax makes no special provision for negative indices in\nsequences; however, built-in sequences all provide a "__getitem__()"\nmethod that interprets negative indices by adding the length of the\nsequence to the index (so that "x[-1]" selects the last item of "x").\nThe resulting value must be a nonnegative integer less than the number\nof items in the sequence, and the subscription selects the item whose\nindex is that value (counting from zero). Since the support for\nnegative indices and slicing occurs in the object\'s "__getitem__()"\nmethod, subclasses overriding this method will need to explicitly add\nthat support.\n\nA string\'s items are characters.  A character is not a separate data\ntype but a string of exactly one character.\n',
  'truth': u'\nTruth Value Testing\n*******************\n\nAny object can be tested for truth value, for use in an "if" or\n"while" condition or as operand of the Boolean operations below. The\nfollowing values are considered false:\n\n* "None"\n\n* "False"\n\n* zero of any numeric type, for example, "0", "0.0", "0j".\n\n* any empty sequence, for example, "\'\'", "()", "[]".\n\n* any empty mapping, for example, "{}".\n\n* instances of user-defined classes, if the class defines a\n  "__bool__()" or "__len__()" method, when that method returns the\n  integer zero or "bool" value "False". [1]\n\nAll other values are considered true --- so objects of many types are\nalways true.\n\nOperations and built-in functions that have a Boolean result always\nreturn "0" or "False" for false and "1" or "True" for true, unless\notherwise stated. (Important exception: the Boolean operations "or"\nand "and" always return one of their operands.)\n',
  'try': u'\nThe "try" statement\n*******************\n\nThe "try" statement specifies exception handlers and/or cleanup code\nfor a group of statements:\n\n   try_stmt  ::= try1_stmt | try2_stmt\n   try1_stmt ::= "try" ":" suite\n                 ("except" [expression ["as" identifier]] ":" suite)+\n                 ["else" ":" suite]\n                 ["finally" ":" suite]\n   try2_stmt ::= "try" ":" suite\n                 "finally" ":" suite\n\nThe "except" clause(s) specify one or more exception handlers. When no\nexception occurs in the "try" clause, no exception handler is\nexecuted. When an exception occurs in the "try" suite, a search for an\nexception handler is started.  This search inspects the except clauses\nin turn until one is found that matches the exception.  An expression-\nless except clause, if present, must be last; it matches any\nexception.  For an except clause with an expression, that expression\nis evaluated, and the clause matches the exception if the resulting\nobject is "compatible" with the exception.  An object is compatible\nwith an exception if it is the class or a base class of the exception\nobject or a tuple containing an item compatible with the exception.\n\nIf no except clause matches the exception, the search for an exception\nhandler continues in the surrounding code and on the invocation stack.\n[1]\n\nIf the evaluation of an expression in the header of an except clause\nraises an exception, the original search for a handler is canceled and\na search starts for the new exception in the surrounding code and on\nthe call stack (it is treated as if the entire "try" statement raised\nthe exception).\n\nWhen a matching except clause is found, the exception is assigned to\nthe target specified after the "as" keyword in that except clause, if\npresent, and the except clause\'s suite is executed.  All except\nclauses must have an executable block.  When the end of this block is\nreached, execution continues normally after the entire try statement.\n(This means that if two nested handlers exist for the same exception,\nand the exception occurs in the try clause of the inner handler, the\nouter handler will not handle the exception.)\n\nWhen an exception has been assigned using "as target", it is cleared\nat the end of the except clause.  This is as if\n\n   except E as N:\n       foo\n\nwas translated to\n\n   except E as N:\n       try:\n           foo\n       finally:\n           del N\n\nThis means the exception must be assigned to a different name to be\nable to refer to it after the except clause.  Exceptions are cleared\nbecause with the traceback attached to them, they form a reference\ncycle with the stack frame, keeping all locals in that frame alive\nuntil the next garbage collection occurs.\n\nBefore an except clause\'s suite is executed, details about the\nexception are stored in the "sys" module and can be accessed via\n"sys.exc_info()". "sys.exc_info()" returns a 3-tuple consisting of the\nexception class, the exception instance and a traceback object (see\nsection *The standard type hierarchy*) identifying the point in the\nprogram where the exception occurred.  "sys.exc_info()" values are\nrestored to their previous values (before the call) when returning\nfrom a function that handled an exception.\n\nThe optional "else" clause is executed if and when control flows off\nthe end of the "try" clause. [2] Exceptions in the "else" clause are\nnot handled by the preceding "except" clauses.\n\nIf "finally" is present, it specifies a \'cleanup\' handler.  The "try"\nclause is executed, including any "except" and "else" clauses.  If an\nexception occurs in any of the clauses and is not handled, the\nexception is temporarily saved. The "finally" clause is executed.  If\nthere is a saved exception it is re-raised at the end of the "finally"\nclause.  If the "finally" clause raises another exception, the saved\nexception is set as the context of the new exception. If the "finally"\nclause executes a "return" or "break" statement, the saved exception\nis discarded:\n\n   >>> def f():\n   ...     try:\n   ...         1/0\n   ...     finally:\n   ...         return 42\n   ...\n   >>> f()\n   42\n\nThe exception information is not available to the program during\nexecution of the "finally" clause.\n\nWhen a "return", "break" or "continue" statement is executed in the\n"try" suite of a "try"..."finally" statement, the "finally" clause is\nalso executed \'on the way out.\' A "continue" statement is illegal in\nthe "finally" clause. (The reason is a problem with the current\nimplementation --- this restriction may be lifted in the future).\n\nThe return value of a function is determined by the last "return"\nstatement executed.  Since the "finally" clause always executes, a\n"return" statement executed in the "finally" clause will always be the\nlast one executed:\n\n   >>> def foo():\n   ...     try:\n   ...         return \'try\'\n   ...     finally:\n   ...         return \'finally\'\n   ...\n   >>> foo()\n   \'finally\'\n\nAdditional information on exceptions can be found in section\n*Exceptions*, and information on using the "raise" statement to\ngenerate exceptions may be found in section *The raise statement*.\n',
- 'types': u'\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python.  Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types.  Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.), although such additions\nwill often be provided via the standard library instead.\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\'  These are attributes that provide access to the\nimplementation and are not intended for general use.  Their definition\nmay change in the future.\n\nNone\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name "None". It\n   is used to signify the absence of a value in many situations, e.g.,\n   it is returned from functions that don\'t explicitly return\n   anything. Its truth value is false.\n\nNotImplemented\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name\n   "NotImplemented". Numeric methods and rich comparison methods\n   should return this value if they do not implement the operation for\n   the operands provided.  (The interpreter will then try the\n   reflected operation, or some other fallback, depending on the\n   operator.)  Its truth value is true.\n\n   See *Implementing the arithmetic operations* for more details.\n\nEllipsis\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the literal "..." or the\n   built-in name "Ellipsis".  Its truth value is true.\n\n"numbers.Number"\n   These are created by numeric literals and returned as results by\n   arithmetic operators and arithmetic built-in functions.  Numeric\n   objects are immutable; once created their value never changes.\n   Python numbers are of course strongly related to mathematical\n   numbers, but subject to the limitations of numerical representation\n   in computers.\n\n   Python distinguishes between integers, floating point numbers, and\n   complex numbers:\n\n   "numbers.Integral"\n      These represent elements from the mathematical set of integers\n      (positive and negative).\n\n      There are two types of integers:\n\n      Integers ("int")\n\n         These represent numbers in an unlimited range, subject to\n         available (virtual) memory only.  For the purpose of shift\n         and mask operations, a binary representation is assumed, and\n         negative numbers are represented in a variant of 2\'s\n         complement which gives the illusion of an infinite string of\n         sign bits extending to the left.\n\n      Booleans ("bool")\n         These represent the truth values False and True.  The two\n         objects representing the values "False" and "True" are the\n         only Boolean objects. The Boolean type is a subtype of the\n         integer type, and Boolean values behave like the values 0 and\n         1, respectively, in almost all contexts, the exception being\n         that when converted to a string, the strings ""False"" or\n         ""True"" are returned, respectively.\n\n      The rules for integer representation are intended to give the\n      most meaningful interpretation of shift and mask operations\n      involving negative integers.\n\n   "numbers.Real" ("float")\n      These represent machine-level double precision floating point\n      numbers. You are at the mercy of the underlying machine\n      architecture (and C or Java implementation) for the accepted\n      range and handling of overflow. Python does not support single-\n      precision floating point numbers; the savings in processor and\n      memory usage that are usually the reason for using these are\n      dwarfed by the overhead of using objects in Python, so there is\n      no reason to complicate the language with two kinds of floating\n      point numbers.\n\n   "numbers.Complex" ("complex")\n      These represent complex numbers as a pair of machine-level\n      double precision floating point numbers.  The same caveats apply\n      as for floating point numbers. The real and imaginary parts of a\n      complex number "z" can be retrieved through the read-only\n      attributes "z.real" and "z.imag".\n\nSequences\n   These represent finite ordered sets indexed by non-negative\n   numbers. The built-in function "len()" returns the number of items\n   of a sequence. When the length of a sequence is *n*, the index set\n   contains the numbers 0, 1, ..., *n*-1.  Item *i* of sequence *a* is\n   selected by "a[i]".\n\n   Sequences also support slicing: "a[i:j]" selects all items with\n   index *k* such that *i* "<=" *k* "<" *j*.  When used as an\n   expression, a slice is a sequence of the same type.  This implies\n   that the index set is renumbered so that it starts at 0.\n\n   Some sequences also support "extended slicing" with a third "step"\n   parameter: "a[i:j:k]" selects all items of *a* with index *x* where\n   "x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.\n\n   Sequences are distinguished according to their mutability:\n\n   Immutable sequences\n      An object of an immutable sequence type cannot change once it is\n      created.  (If the object contains references to other objects,\n      these other objects may be mutable and may be changed; however,\n      the collection of objects directly referenced by an immutable\n      object cannot change.)\n\n      The following types are immutable sequences:\n\n      Strings\n         A string is a sequence of values that represent Unicode code\n         points. All the code points in the range "U+0000 - U+10FFFF"\n         can be represented in a string.  Python doesn\'t have a "char"\n         type; instead, every code point in the string is represented\n         as a string object with length "1".  The built-in function\n         "ord()" converts a code point from its string form to an\n         integer in the range "0 - 10FFFF"; "chr()" converts an\n         integer in the range "0 - 10FFFF" to the corresponding length\n         "1" string object. "str.encode()" can be used to convert a\n         "str" to "bytes" using the given text encoding, and\n         "bytes.decode()" can be used to achieve the opposite.\n\n      Tuples\n         The items of a tuple are arbitrary Python objects. Tuples of\n         two or more items are formed by comma-separated lists of\n         expressions.  A tuple of one item (a \'singleton\') can be\n         formed by affixing a comma to an expression (an expression by\n         itself does not create a tuple, since parentheses must be\n         usable for grouping of expressions).  An empty tuple can be\n         formed by an empty pair of parentheses.\n\n      Bytes\n         A bytes object is an immutable array.  The items are 8-bit\n         bytes, represented by integers in the range 0 <= x < 256.\n         Bytes literals (like "b\'abc\'") and the built-in function\n         "bytes()" can be used to construct bytes objects.  Also,\n         bytes objects can be decoded to strings via the "decode()"\n         method.\n\n   Mutable sequences\n      Mutable sequences can be changed after they are created.  The\n      subscription and slicing notations can be used as the target of\n      assignment and "del" (delete) statements.\n\n      There are currently two intrinsic mutable sequence types:\n\n      Lists\n         The items of a list are arbitrary Python objects.  Lists are\n         formed by placing a comma-separated list of expressions in\n         square brackets. (Note that there are no special cases needed\n         to form lists of length 0 or 1.)\n\n      Byte Arrays\n         A bytearray object is a mutable array. They are created by\n         the built-in "bytearray()" constructor.  Aside from being\n         mutable (and hence unhashable), byte arrays otherwise provide\n         the same interface and functionality as immutable bytes\n         objects.\n\n      The extension module "array" provides an additional example of a\n      mutable sequence type, as does the "collections" module.\n\nSet types\n   These represent unordered, finite sets of unique, immutable\n   objects. As such, they cannot be indexed by any subscript. However,\n   they can be iterated over, and the built-in function "len()"\n   returns the number of items in a set. Common uses for sets are fast\n   membership testing, removing duplicates from a sequence, and\n   computing mathematical operations such as intersection, union,\n   difference, and symmetric difference.\n\n   For set elements, the same immutability rules apply as for\n   dictionary keys. Note that numeric types obey the normal rules for\n   numeric comparison: if two numbers compare equal (e.g., "1" and\n   "1.0"), only one of them can be contained in a set.\n\n   There are currently two intrinsic set types:\n\n   Sets\n      These represent a mutable set. They are created by the built-in\n      "set()" constructor and can be modified afterwards by several\n      methods, such as "add()".\n\n   Frozen sets\n      These represent an immutable set.  They are created by the\n      built-in "frozenset()" constructor.  As a frozenset is immutable\n      and *hashable*, it can be used again as an element of another\n      set, or as a dictionary key.\n\nMappings\n   These represent finite sets of objects indexed by arbitrary index\n   sets. The subscript notation "a[k]" selects the item indexed by "k"\n   from the mapping "a"; this can be used in expressions and as the\n   target of assignments or "del" statements. The built-in function\n   "len()" returns the number of items in a mapping.\n\n   There is currently a single intrinsic mapping type:\n\n   Dictionaries\n      These represent finite sets of objects indexed by nearly\n      arbitrary values.  The only types of values not acceptable as\n      keys are values containing lists or dictionaries or other\n      mutable types that are compared by value rather than by object\n      identity, the reason being that the efficient implementation of\n      dictionaries requires a key\'s hash value to remain constant.\n      Numeric types used for keys obey the normal rules for numeric\n      comparison: if two numbers compare equal (e.g., "1" and "1.0")\n      then they can be used interchangeably to index the same\n      dictionary entry.\n\n      Dictionaries are mutable; they can be created by the "{...}"\n      notation (see section *Dictionary displays*).\n\n      The extension modules "dbm.ndbm" and "dbm.gnu" provide\n      additional examples of mapping types, as does the "collections"\n      module.\n\nCallable types\n   These are the types to which the function call operation (see\n   section *Calls*) can be applied:\n\n   User-defined functions\n      A user-defined function object is created by a function\n      definition (see section *Function definitions*).  It should be\n      called with an argument list containing the same number of items\n      as the function\'s formal parameter list.\n\n      Special attributes:\n\n      +---------------------------+---------------------------------+-------------+\n      | Attribute                 | Meaning                         |             |\n      +===========================+=================================+=============+\n      | "__doc__"                 | The function\'s documentation    | Writable    |\n      |                           | string, or "None" if            |             |\n      |                           | unavailable; not inherited by   |             |\n      |                           | subclasses                      |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__name__"                | The function\'s name             | Writable    |\n      +---------------------------+---------------------------------+-------------+\n      | "__qualname__"            | The function\'s *qualified name* | Writable    |\n      |                           | New in version 3.3.             |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__module__"              | The name of the module the      | Writable    |\n      |                           | function was defined in, or     |             |\n      |                           | "None" if unavailable.          |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__defaults__"            | A tuple containing default      | Writable    |\n      |                           | argument values for those       |             |\n      |                           | arguments that have defaults,   |             |\n      |                           | or "None" if no arguments have  |             |\n      |                           | a default value                 |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__code__"                | The code object representing    | Writable    |\n      |                           | the compiled function body.     |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__globals__"             | A reference to the dictionary   | Read-only   |\n      |                           | that holds the function\'s       |             |\n      |                           | global variables --- the global |             |\n      |                           | namespace of the module in      |             |\n      |                           | which the function was defined. |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__dict__"                | The namespace supporting        | Writable    |\n      |                           | arbitrary function attributes.  |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__closure__"             | "None" or a tuple of cells that | Read-only   |\n      |                           | contain bindings for the        |             |\n      |                           | function\'s free variables.      |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__annotations__"         | A dict containing annotations   | Writable    |\n      |                           | of parameters.  The keys of the |             |\n      |                           | dict are the parameter names,   |             |\n      |                           | and "\'return\'" for the return   |             |\n      |                           | annotation, if provided.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__kwdefaults__"          | A dict containing defaults for  | Writable    |\n      |                           | keyword-only parameters.        |             |\n      +---------------------------+---------------------------------+-------------+\n\n      Most of the attributes labelled "Writable" check the type of the\n      assigned value.\n\n      Function objects also support getting and setting arbitrary\n      attributes, which can be used, for example, to attach metadata\n      to functions.  Regular attribute dot-notation is used to get and\n      set such attributes. *Note that the current implementation only\n      supports function attributes on user-defined functions. Function\n      attributes on built-in functions may be supported in the\n      future.*\n\n      Additional information about a function\'s definition can be\n      retrieved from its code object; see the description of internal\n      types below.\n\n   Instance methods\n      An instance method object combines a class, a class instance and\n      any callable object (normally a user-defined function).\n\n      Special read-only attributes: "__self__" is the class instance\n      object, "__func__" is the function object; "__doc__" is the\n      method\'s documentation (same as "__func__.__doc__"); "__name__"\n      is the method name (same as "__func__.__name__"); "__module__"\n      is the name of the module the method was defined in, or "None"\n      if unavailable.\n\n      Methods also support accessing (but not setting) the arbitrary\n      function attributes on the underlying function object.\n\n      User-defined method objects may be created when getting an\n      attribute of a class (perhaps via an instance of that class), if\n      that attribute is a user-defined function object or a class\n      method object.\n\n      When an instance method object is created by retrieving a user-\n      defined function object from a class via one of its instances,\n      its "__self__" attribute is the instance, and the method object\n      is said to be bound.  The new method\'s "__func__" attribute is\n      the original function object.\n\n      When a user-defined method object is created by retrieving\n      another method object from a class or instance, the behaviour is\n      the same as for a function object, except that the "__func__"\n      attribute of the new instance is not the original method object\n      but its "__func__" attribute.\n\n      When an instance method object is created by retrieving a class\n      method object from a class or instance, its "__self__" attribute\n      is the class itself, and its "__func__" attribute is the\n      function object underlying the class method.\n\n      When an instance method object is called, the underlying\n      function ("__func__") is called, inserting the class instance\n      ("__self__") in front of the argument list.  For instance, when\n      "C" is a class which contains a definition for a function "f()",\n      and "x" is an instance of "C", calling "x.f(1)" is equivalent to\n      calling "C.f(x, 1)".\n\n      When an instance method object is derived from a class method\n      object, the "class instance" stored in "__self__" will actually\n      be the class itself, so that calling either "x.f(1)" or "C.f(1)"\n      is equivalent to calling "f(C,1)" where "f" is the underlying\n      function.\n\n      Note that the transformation from function object to instance\n      method object happens each time the attribute is retrieved from\n      the instance.  In some cases, a fruitful optimization is to\n      assign the attribute to a local variable and call that local\n      variable. Also notice that this transformation only happens for\n      user-defined functions; other callable objects (and all non-\n      callable objects) are retrieved without transformation.  It is\n      also important to note that user-defined functions which are\n      attributes of a class instance are not converted to bound\n      methods; this *only* happens when the function is an attribute\n      of the class.\n\n   Generator functions\n      A function or method which uses the "yield" statement (see\n      section *The yield statement*) is called a *generator function*.\n      Such a function, when called, always returns an iterator object\n      which can be used to execute the body of the function:  calling\n      the iterator\'s "iterator.__next__()" method will cause the\n      function to execute until it provides a value using the "yield"\n      statement.  When the function executes a "return" statement or\n      falls off the end, a "StopIteration" exception is raised and the\n      iterator will have reached the end of the set of values to be\n      returned.\n\n   Coroutine functions\n      A function or method which is defined using "async def" is\n      called a *coroutine function*.  Such a function, when called,\n      returns a *coroutine* object.  It may contain "await"\n      expressions, as well as "async with" and "async for" statements.\n      See also *Coroutines* section.\n\n   Built-in functions\n      A built-in function object is a wrapper around a C function.\n      Examples of built-in functions are "len()" and "math.sin()"\n      ("math" is a standard built-in module). The number and type of\n      the arguments are determined by the C function. Special read-\n      only attributes: "__doc__" is the function\'s documentation\n      string, or "None" if unavailable; "__name__" is the function\'s\n      name; "__self__" is set to "None" (but see the next item);\n      "__module__" is the name of the module the function was defined\n      in or "None" if unavailable.\n\n   Built-in methods\n      This is really a different disguise of a built-in function, this\n      time containing an object passed to the C function as an\n      implicit extra argument.  An example of a built-in method is\n      "alist.append()", assuming *alist* is a list object. In this\n      case, the special read-only attribute "__self__" is set to the\n      object denoted by *alist*.\n\n   Classes\n      Classes are callable.  These objects normally act as factories\n      for new instances of themselves, but variations are possible for\n      class types that override "__new__()".  The arguments of the\n      call are passed to "__new__()" and, in the typical case, to\n      "__init__()" to initialize the new instance.\n\n   Class Instances\n      Instances of arbitrary classes can be made callable by defining\n      a "__call__()" method in their class.\n\nModules\n   Modules are a basic organizational unit of Python code, and are\n   created by the *import system* as invoked either by the "import"\n   statement (see "import"), or by calling functions such as\n   "importlib.import_module()" and built-in "__import__()".  A module\n   object has a namespace implemented by a dictionary object (this is\n   the dictionary referenced by the "__globals__" attribute of\n   functions defined in the module).  Attribute references are\n   translated to lookups in this dictionary, e.g., "m.x" is equivalent\n   to "m.__dict__["x"]". A module object does not contain the code\n   object used to initialize the module (since it isn\'t needed once\n   the initialization is done).\n\n   Attribute assignment updates the module\'s namespace dictionary,\n   e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".\n\n   Special read-only attribute: "__dict__" is the module\'s namespace\n   as a dictionary object.\n\n   **CPython implementation detail:** Because of the way CPython\n   clears module dictionaries, the module dictionary will be cleared\n   when the module falls out of scope even if the dictionary still has\n   live references.  To avoid this, copy the dictionary or keep the\n   module around while using its dictionary directly.\n\n   Predefined (writable) attributes: "__name__" is the module\'s name;\n   "__doc__" is the module\'s documentation string, or "None" if\n   unavailable; "__file__" is the pathname of the file from which the\n   module was loaded, if it was loaded from a file. The "__file__"\n   attribute may be missing for certain types of modules, such as C\n   modules that are statically linked into the interpreter; for\n   extension modules loaded dynamically from a shared library, it is\n   the pathname of the shared library file.\n\nCustom classes\n   Custom class types are typically created by class definitions (see\n   section *Class definitions*).  A class has a namespace implemented\n   by a dictionary object. Class attribute references are translated\n   to lookups in this dictionary, e.g., "C.x" is translated to\n   "C.__dict__["x"]" (although there are a number of hooks which allow\n   for other means of locating attributes). When the attribute name is\n   not found there, the attribute search continues in the base\n   classes. This search of the base classes uses the C3 method\n   resolution order which behaves correctly even in the presence of\n   \'diamond\' inheritance structures where there are multiple\n   inheritance paths leading back to a common ancestor. Additional\n   details on the C3 MRO used by Python can be found in the\n   documentation accompanying the 2.3 release at\n   https://www.python.org/download/releases/2.3/mro/.\n\n   When a class attribute reference (for class "C", say) would yield a\n   class method object, it is transformed into an instance method\n   object whose "__self__" attributes is "C".  When it would yield a\n   static method object, it is transformed into the object wrapped by\n   the static method object. See section *Implementing Descriptors*\n   for another way in which attributes retrieved from a class may\n   differ from those actually contained in its "__dict__".\n\n   Class attribute assignments update the class\'s dictionary, never\n   the dictionary of a base class.\n\n   A class object can be called (see above) to yield a class instance\n   (see below).\n\n   Special attributes: "__name__" is the class name; "__module__" is\n   the module name in which the class was defined; "__dict__" is the\n   dictionary containing the class\'s namespace; "__bases__" is a tuple\n   (possibly empty or a singleton) containing the base classes, in the\n   order of their occurrence in the base class list; "__doc__" is the\n   class\'s documentation string, or None if undefined.\n\nClass instances\n   A class instance is created by calling a class object (see above).\n   A class instance has a namespace implemented as a dictionary which\n   is the first place in which attribute references are searched.\n   When an attribute is not found there, and the instance\'s class has\n   an attribute by that name, the search continues with the class\n   attributes.  If a class attribute is found that is a user-defined\n   function object, it is transformed into an instance method object\n   whose "__self__" attribute is the instance.  Static method and\n   class method objects are also transformed; see above under\n   "Classes".  See section *Implementing Descriptors* for another way\n   in which attributes of a class retrieved via its instances may\n   differ from the objects actually stored in the class\'s "__dict__".\n   If no class attribute is found, and the object\'s class has a\n   "__getattr__()" method, that is called to satisfy the lookup.\n\n   Attribute assignments and deletions update the instance\'s\n   dictionary, never a class\'s dictionary.  If the class has a\n   "__setattr__()" or "__delattr__()" method, this is called instead\n   of updating the instance dictionary directly.\n\n   Class instances can pretend to be numbers, sequences, or mappings\n   if they have methods with certain special names.  See section\n   *Special method names*.\n\n   Special attributes: "__dict__" is the attribute dictionary;\n   "__class__" is the instance\'s class.\n\nI/O objects (also known as file objects)\n   A *file object* represents an open file.  Various shortcuts are\n   available to create file objects: the "open()" built-in function,\n   and also "os.popen()", "os.fdopen()", and the "makefile()" method\n   of socket objects (and perhaps by other functions or methods\n   provided by extension modules).\n\n   The objects "sys.stdin", "sys.stdout" and "sys.stderr" are\n   initialized to file objects corresponding to the interpreter\'s\n   standard input, output and error streams; they are all open in text\n   mode and therefore follow the interface defined by the\n   "io.TextIOBase" abstract class.\n\nInternal types\n   A few types used internally by the interpreter are exposed to the\n   user. Their definitions may change with future versions of the\n   interpreter, but they are mentioned here for completeness.\n\n   Code objects\n      Code objects represent *byte-compiled* executable Python code,\n      or *bytecode*. The difference between a code object and a\n      function object is that the function object contains an explicit\n      reference to the function\'s globals (the module in which it was\n      defined), while a code object contains no context; also the\n      default argument values are stored in the function object, not\n      in the code object (because they represent values calculated at\n      run-time).  Unlike function objects, code objects are immutable\n      and contain no references (directly or indirectly) to mutable\n      objects.\n\n      Special read-only attributes: "co_name" gives the function name;\n      "co_argcount" is the number of positional arguments (including\n      arguments with default values); "co_nlocals" is the number of\n      local variables used by the function (including arguments);\n      "co_varnames" is a tuple containing the names of the local\n      variables (starting with the argument names); "co_cellvars" is a\n      tuple containing the names of local variables that are\n      referenced by nested functions; "co_freevars" is a tuple\n      containing the names of free variables; "co_code" is a string\n      representing the sequence of bytecode instructions; "co_consts"\n      is a tuple containing the literals used by the bytecode;\n      "co_names" is a tuple containing the names used by the bytecode;\n      "co_filename" is the filename from which the code was compiled;\n      "co_firstlineno" is the first line number of the function;\n      "co_lnotab" is a string encoding the mapping from bytecode\n      offsets to line numbers (for details see the source code of the\n      interpreter); "co_stacksize" is the required stack size\n      (including local variables); "co_flags" is an integer encoding a\n      number of flags for the interpreter.\n\n      The following flag bits are defined for "co_flags": bit "0x04"\n      is set if the function uses the "*arguments" syntax to accept an\n      arbitrary number of positional arguments; bit "0x08" is set if\n      the function uses the "**keywords" syntax to accept arbitrary\n      keyword arguments; bit "0x20" is set if the function is a\n      generator.\n\n      Future feature declarations ("from __future__ import division")\n      also use bits in "co_flags" to indicate whether a code object\n      was compiled with a particular feature enabled: bit "0x2000" is\n      set if the function was compiled with future division enabled;\n      bits "0x10" and "0x1000" were used in earlier versions of\n      Python.\n\n      Other bits in "co_flags" are reserved for internal use.\n\n      If a code object represents a function, the first item in\n      "co_consts" is the documentation string of the function, or\n      "None" if undefined.\n\n   Frame objects\n      Frame objects represent execution frames.  They may occur in\n      traceback objects (see below).\n\n      Special read-only attributes: "f_back" is to the previous stack\n      frame (towards the caller), or "None" if this is the bottom\n      stack frame; "f_code" is the code object being executed in this\n      frame; "f_locals" is the dictionary used to look up local\n      variables; "f_globals" is used for global variables;\n      "f_builtins" is used for built-in (intrinsic) names; "f_lasti"\n      gives the precise instruction (this is an index into the\n      bytecode string of the code object).\n\n      Special writable attributes: "f_trace", if not "None", is a\n      function called at the start of each source code line (this is\n      used by the debugger); "f_lineno" is the current line number of\n      the frame --- writing to this from within a trace function jumps\n      to the given line (only for the bottom-most frame).  A debugger\n      can implement a Jump command (aka Set Next Statement) by writing\n      to f_lineno.\n\n      Frame objects support one method:\n\n      frame.clear()\n\n         This method clears all references to local variables held by\n         the frame.  Also, if the frame belonged to a generator, the\n         generator is finalized.  This helps break reference cycles\n         involving frame objects (for example when catching an\n         exception and storing its traceback for later use).\n\n         "RuntimeError" is raised if the frame is currently executing.\n\n         New in version 3.4.\n\n   Traceback objects\n      Traceback objects represent a stack trace of an exception.  A\n      traceback object is created when an exception occurs.  When the\n      search for an exception handler unwinds the execution stack, at\n      each unwound level a traceback object is inserted in front of\n      the current traceback.  When an exception handler is entered,\n      the stack trace is made available to the program. (See section\n      *The try statement*.) It is accessible as the third item of the\n      tuple returned by "sys.exc_info()". When the program contains no\n      suitable handler, the stack trace is written (nicely formatted)\n      to the standard error stream; if the interpreter is interactive,\n      it is also made available to the user as "sys.last_traceback".\n\n      Special read-only attributes: "tb_next" is the next level in the\n      stack trace (towards the frame where the exception occurred), or\n      "None" if there is no next level; "tb_frame" points to the\n      execution frame of the current level; "tb_lineno" gives the line\n      number where the exception occurred; "tb_lasti" indicates the\n      precise instruction.  The line number and last instruction in\n      the traceback may differ from the line number of its frame\n      object if the exception occurred in a "try" statement with no\n      matching except clause or with a finally clause.\n\n   Slice objects\n      Slice objects are used to represent slices for "__getitem__()"\n      methods.  They are also created by the built-in "slice()"\n      function.\n\n      Special read-only attributes: "start" is the lower bound; "stop"\n      is the upper bound; "step" is the step value; each is "None" if\n      omitted.  These attributes can have any type.\n\n      Slice objects support one method:\n\n      slice.indices(self, length)\n\n         This method takes a single integer argument *length* and\n         computes information about the slice that the slice object\n         would describe if applied to a sequence of *length* items.\n         It returns a tuple of three integers; respectively these are\n         the *start* and *stop* indices and the *step* or stride\n         length of the slice. Missing or out-of-bounds indices are\n         handled in a manner consistent with regular slices.\n\n   Static method objects\n      Static method objects provide a way of defeating the\n      transformation of function objects to method objects described\n      above. A static method object is a wrapper around any other\n      object, usually a user-defined method object. When a static\n      method object is retrieved from a class or a class instance, the\n      object actually returned is the wrapped object, which is not\n      subject to any further transformation. Static method objects are\n      not themselves callable, although the objects they wrap usually\n      are. Static method objects are created by the built-in\n      "staticmethod()" constructor.\n\n   Class method objects\n      A class method object, like a static method object, is a wrapper\n      around another object that alters the way in which that object\n      is retrieved from classes and class instances. The behaviour of\n      class method objects upon such retrieval is described above,\n      under "User-defined methods". Class method objects are created\n      by the built-in "classmethod()" constructor.\n',
+ 'types': u'\nThe standard type hierarchy\n***************************\n\nBelow is a list of the types that are built into Python.  Extension\nmodules (written in C, Java, or other languages, depending on the\nimplementation) can define additional types.  Future versions of\nPython may add types to the type hierarchy (e.g., rational numbers,\nefficiently stored arrays of integers, etc.), although such additions\nwill often be provided via the standard library instead.\n\nSome of the type descriptions below contain a paragraph listing\n\'special attributes.\'  These are attributes that provide access to the\nimplementation and are not intended for general use.  Their definition\nmay change in the future.\n\nNone\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name "None". It\n   is used to signify the absence of a value in many situations, e.g.,\n   it is returned from functions that don\'t explicitly return\n   anything. Its truth value is false.\n\nNotImplemented\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the built-in name\n   "NotImplemented". Numeric methods and rich comparison methods\n   should return this value if they do not implement the operation for\n   the operands provided.  (The interpreter will then try the\n   reflected operation, or some other fallback, depending on the\n   operator.)  Its truth value is true.\n\n   See *Implementing the arithmetic operations* for more details.\n\nEllipsis\n   This type has a single value.  There is a single object with this\n   value. This object is accessed through the literal "..." or the\n   built-in name "Ellipsis".  Its truth value is true.\n\n"numbers.Number"\n   These are created by numeric literals and returned as results by\n   arithmetic operators and arithmetic built-in functions.  Numeric\n   objects are immutable; once created their value never changes.\n   Python numbers are of course strongly related to mathematical\n   numbers, but subject to the limitations of numerical representation\n   in computers.\n\n   Python distinguishes between integers, floating point numbers, and\n   complex numbers:\n\n   "numbers.Integral"\n      These represent elements from the mathematical set of integers\n      (positive and negative).\n\n      There are two types of integers:\n\n      Integers ("int")\n\n         These represent numbers in an unlimited range, subject to\n         available (virtual) memory only.  For the purpose of shift\n         and mask operations, a binary representation is assumed, and\n         negative numbers are represented in a variant of 2\'s\n         complement which gives the illusion of an infinite string of\n         sign bits extending to the left.\n\n      Booleans ("bool")\n         These represent the truth values False and True.  The two\n         objects representing the values "False" and "True" are the\n         only Boolean objects. The Boolean type is a subtype of the\n         integer type, and Boolean values behave like the values 0 and\n         1, respectively, in almost all contexts, the exception being\n         that when converted to a string, the strings ""False"" or\n         ""True"" are returned, respectively.\n\n      The rules for integer representation are intended to give the\n      most meaningful interpretation of shift and mask operations\n      involving negative integers.\n\n   "numbers.Real" ("float")\n      These represent machine-level double precision floating point\n      numbers. You are at the mercy of the underlying machine\n      architecture (and C or Java implementation) for the accepted\n      range and handling of overflow. Python does not support single-\n      precision floating point numbers; the savings in processor and\n      memory usage that are usually the reason for using these are\n      dwarfed by the overhead of using objects in Python, so there is\n      no reason to complicate the language with two kinds of floating\n      point numbers.\n\n   "numbers.Complex" ("complex")\n      These represent complex numbers as a pair of machine-level\n      double precision floating point numbers.  The same caveats apply\n      as for floating point numbers. The real and imaginary parts of a\n      complex number "z" can be retrieved through the read-only\n      attributes "z.real" and "z.imag".\n\nSequences\n   These represent finite ordered sets indexed by non-negative\n   numbers. The built-in function "len()" returns the number of items\n   of a sequence. When the length of a sequence is *n*, the index set\n   contains the numbers 0, 1, ..., *n*-1.  Item *i* of sequence *a* is\n   selected by "a[i]".\n\n   Sequences also support slicing: "a[i:j]" selects all items with\n   index *k* such that *i* "<=" *k* "<" *j*.  When used as an\n   expression, a slice is a sequence of the same type.  This implies\n   that the index set is renumbered so that it starts at 0.\n\n   Some sequences also support "extended slicing" with a third "step"\n   parameter: "a[i:j:k]" selects all items of *a* with index *x* where\n   "x = i + n*k", *n* ">=" "0" and *i* "<=" *x* "<" *j*.\n\n   Sequences are distinguished according to their mutability:\n\n   Immutable sequences\n      An object of an immutable sequence type cannot change once it is\n      created.  (If the object contains references to other objects,\n      these other objects may be mutable and may be changed; however,\n      the collection of objects directly referenced by an immutable\n      object cannot change.)\n\n      The following types are immutable sequences:\n\n      Strings\n         A string is a sequence of values that represent Unicode code\n         points. All the code points in the range "U+0000 - U+10FFFF"\n         can be represented in a string.  Python doesn\'t have a "char"\n         type; instead, every code point in the string is represented\n         as a string object with length "1".  The built-in function\n         "ord()" converts a code point from its string form to an\n         integer in the range "0 - 10FFFF"; "chr()" converts an\n         integer in the range "0 - 10FFFF" to the corresponding length\n         "1" string object. "str.encode()" can be used to convert a\n         "str" to "bytes" using the given text encoding, and\n         "bytes.decode()" can be used to achieve the opposite.\n\n      Tuples\n         The items of a tuple are arbitrary Python objects. Tuples of\n         two or more items are formed by comma-separated lists of\n         expressions.  A tuple of one item (a \'singleton\') can be\n         formed by affixing a comma to an expression (an expression by\n         itself does not create a tuple, since parentheses must be\n         usable for grouping of expressions).  An empty tuple can be\n         formed by an empty pair of parentheses.\n\n      Bytes\n         A bytes object is an immutable array.  The items are 8-bit\n         bytes, represented by integers in the range 0 <= x < 256.\n         Bytes literals (like "b\'abc\'") and the built-in function\n         "bytes()" can be used to construct bytes objects.  Also,\n         bytes objects can be decoded to strings via the "decode()"\n         method.\n\n   Mutable sequences\n      Mutable sequences can be changed after they are created.  The\n      subscription and slicing notations can be used as the target of\n      assignment and "del" (delete) statements.\n\n      There are currently two intrinsic mutable sequence types:\n\n      Lists\n         The items of a list are arbitrary Python objects.  Lists are\n         formed by placing a comma-separated list of expressions in\n         square brackets. (Note that there are no special cases needed\n         to form lists of length 0 or 1.)\n\n      Byte Arrays\n         A bytearray object is a mutable array. They are created by\n         the built-in "bytearray()" constructor.  Aside from being\n         mutable (and hence unhashable), byte arrays otherwise provide\n         the same interface and functionality as immutable bytes\n         objects.\n\n      The extension module "array" provides an additional example of a\n      mutable sequence type, as does the "collections" module.\n\nSet types\n   These represent unordered, finite sets of unique, immutable\n   objects. As such, they cannot be indexed by any subscript. However,\n   they can be iterated over, and the built-in function "len()"\n   returns the number of items in a set. Common uses for sets are fast\n   membership testing, removing duplicates from a sequence, and\n   computing mathematical operations such as intersection, union,\n   difference, and symmetric difference.\n\n   For set elements, the same immutability rules apply as for\n   dictionary keys. Note that numeric types obey the normal rules for\n   numeric comparison: if two numbers compare equal (e.g., "1" and\n   "1.0"), only one of them can be contained in a set.\n\n   There are currently two intrinsic set types:\n\n   Sets\n      These represent a mutable set. They are created by the built-in\n      "set()" constructor and can be modified afterwards by several\n      methods, such as "add()".\n\n   Frozen sets\n      These represent an immutable set.  They are created by the\n      built-in "frozenset()" constructor.  As a frozenset is immutable\n      and *hashable*, it can be used again as an element of another\n      set, or as a dictionary key.\n\nMappings\n   These represent finite sets of objects indexed by arbitrary index\n   sets. The subscript notation "a[k]" selects the item indexed by "k"\n   from the mapping "a"; this can be used in expressions and as the\n   target of assignments or "del" statements. The built-in function\n   "len()" returns the number of items in a mapping.\n\n   There is currently a single intrinsic mapping type:\n\n   Dictionaries\n      These represent finite sets of objects indexed by nearly\n      arbitrary values.  The only types of values not acceptable as\n      keys are values containing lists or dictionaries or other\n      mutable types that are compared by value rather than by object\n      identity, the reason being that the efficient implementation of\n      dictionaries requires a key\'s hash value to remain constant.\n      Numeric types used for keys obey the normal rules for numeric\n      comparison: if two numbers compare equal (e.g., "1" and "1.0")\n      then they can be used interchangeably to index the same\n      dictionary entry.\n\n      Dictionaries are mutable; they can be created by the "{...}"\n      notation (see section *Dictionary displays*).\n\n      The extension modules "dbm.ndbm" and "dbm.gnu" provide\n      additional examples of mapping types, as does the "collections"\n      module.\n\nCallable types\n   These are the types to which the function call operation (see\n   section *Calls*) can be applied:\n\n   User-defined functions\n      A user-defined function object is created by a function\n      definition (see section *Function definitions*).  It should be\n      called with an argument list containing the same number of items\n      as the function\'s formal parameter list.\n\n      Special attributes:\n\n      +---------------------------+---------------------------------+-------------+\n      | Attribute                 | Meaning                         |             |\n      +===========================+=================================+=============+\n      | "__doc__"                 | The function\'s documentation    | Writable    |\n      |                           | string, or "None" if            |             |\n      |                           | unavailable; not inherited by   |             |\n      |                           | subclasses                      |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__name__"                | The function\'s name             | Writable    |\n      +---------------------------+---------------------------------+-------------+\n      | "__qualname__"            | The function\'s *qualified name* | Writable    |\n      |                           | New in version 3.3.             |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__module__"              | The name of the module the      | Writable    |\n      |                           | function was defined in, or     |             |\n      |                           | "None" if unavailable.          |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__defaults__"            | A tuple containing default      | Writable    |\n      |                           | argument values for those       |             |\n      |                           | arguments that have defaults,   |             |\n      |                           | or "None" if no arguments have  |             |\n      |                           | a default value                 |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__code__"                | The code object representing    | Writable    |\n      |                           | the compiled function body.     |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__globals__"             | A reference to the dictionary   | Read-only   |\n      |                           | that holds the function\'s       |             |\n      |                           | global variables --- the global |             |\n      |                           | namespace of the module in      |             |\n      |                           | which the function was defined. |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__dict__"                | The namespace supporting        | Writable    |\n      |                           | arbitrary function attributes.  |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__closure__"             | "None" or a tuple of cells that | Read-only   |\n      |                           | contain bindings for the        |             |\n      |                           | function\'s free variables.      |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__annotations__"         | A dict containing annotations   | Writable    |\n      |                           | of parameters.  The keys of the |             |\n      |                           | dict are the parameter names,   |             |\n      |                           | and "\'return\'" for the return   |             |\n      |                           | annotation, if provided.        |             |\n      +---------------------------+---------------------------------+-------------+\n      | "__kwdefaults__"          | A dict containing defaults for  | Writable    |\n      |                           | keyword-only parameters.        |             |\n      +---------------------------+---------------------------------+-------------+\n\n      Most of the attributes labelled "Writable" check the type of the\n      assigned value.\n\n      Function objects also support getting and setting arbitrary\n      attributes, which can be used, for example, to attach metadata\n      to functions.  Regular attribute dot-notation is used to get and\n      set such attributes. *Note that the current implementation only\n      supports function attributes on user-defined functions. Function\n      attributes on built-in functions may be supported in the\n      future.*\n\n      Additional information about a function\'s definition can be\n      retrieved from its code object; see the description of internal\n      types below.\n\n   Instance methods\n      An instance method object combines a class, a class instance and\n      any callable object (normally a user-defined function).\n\n      Special read-only attributes: "__self__" is the class instance\n      object, "__func__" is the function object; "__doc__" is the\n      method\'s documentation (same as "__func__.__doc__"); "__name__"\n      is the method name (same as "__func__.__name__"); "__module__"\n      is the name of the module the method was defined in, or "None"\n      if unavailable.\n\n      Methods also support accessing (but not setting) the arbitrary\n      function attributes on the underlying function object.\n\n      User-defined method objects may be created when getting an\n      attribute of a class (perhaps via an instance of that class), if\n      that attribute is a user-defined function object or a class\n      method object.\n\n      When an instance method object is created by retrieving a user-\n      defined function object from a class via one of its instances,\n      its "__self__" attribute is the instance, and the method object\n      is said to be bound.  The new method\'s "__func__" attribute is\n      the original function object.\n\n      When a user-defined method object is created by retrieving\n      another method object from a class or instance, the behaviour is\n      the same as for a function object, except that the "__func__"\n      attribute of the new instance is not the original method object\n      but its "__func__" attribute.\n\n      When an instance method object is created by retrieving a class\n      method object from a class or instance, its "__self__" attribute\n      is the class itself, and its "__func__" attribute is the\n      function object underlying the class method.\n\n      When an instance method object is called, the underlying\n      function ("__func__") is called, inserting the class instance\n      ("__self__") in front of the argument list.  For instance, when\n      "C" is a class which contains a definition for a function "f()",\n      and "x" is an instance of "C", calling "x.f(1)" is equivalent to\n      calling "C.f(x, 1)".\n\n      When an instance method object is derived from a class method\n      object, the "class instance" stored in "__self__" will actually\n      be the class itself, so that calling either "x.f(1)" or "C.f(1)"\n      is equivalent to calling "f(C,1)" where "f" is the underlying\n      function.\n\n      Note that the transformation from function object to instance\n      method object happens each time the attribute is retrieved from\n      the instance.  In some cases, a fruitful optimization is to\n      assign the attribute to a local variable and call that local\n      variable. Also notice that this transformation only happens for\n      user-defined functions; other callable objects (and all non-\n      callable objects) are retrieved without transformation.  It is\n      also important to note that user-defined functions which are\n      attributes of a class instance are not converted to bound\n      methods; this *only* happens when the function is an attribute\n      of the class.\n\n   Generator functions\n      A function or method which uses the "yield" statement (see\n      section *The yield statement*) is called a *generator function*.\n      Such a function, when called, always returns an iterator object\n      which can be used to execute the body of the function:  calling\n      the iterator\'s "iterator.__next__()" method will cause the\n      function to execute until it provides a value using the "yield"\n      statement.  When the function executes a "return" statement or\n      falls off the end, a "StopIteration" exception is raised and the\n      iterator will have reached the end of the set of values to be\n      returned.\n\n   Coroutine functions\n      A function or method which is defined using "async def" is\n      called a *coroutine function*.  Such a function, when called,\n      returns a *coroutine* object.  It may contain "await"\n      expressions, as well as "async with" and "async for" statements.\n      See also the *Coroutine Objects* section.\n\n   Built-in functions\n      A built-in function object is a wrapper around a C function.\n      Examples of built-in functions are "len()" and "math.sin()"\n      ("math" is a standard built-in module). The number and type of\n      the arguments are determined by the C function. Special read-\n      only attributes: "__doc__" is the function\'s documentation\n      string, or "None" if unavailable; "__name__" is the function\'s\n      name; "__self__" is set to "None" (but see the next item);\n      "__module__" is the name of the module the function was defined\n      in or "None" if unavailable.\n\n   Built-in methods\n      This is really a different disguise of a built-in function, this\n      time containing an object passed to the C function as an\n      implicit extra argument.  An example of a built-in method is\n      "alist.append()", assuming *alist* is a list object. In this\n      case, the special read-only attribute "__self__" is set to the\n      object denoted by *alist*.\n\n   Classes\n      Classes are callable.  These objects normally act as factories\n      for new instances of themselves, but variations are possible for\n      class types that override "__new__()".  The arguments of the\n      call are passed to "__new__()" and, in the typical case, to\n      "__init__()" to initialize the new instance.\n\n   Class Instances\n      Instances of arbitrary classes can be made callable by defining\n      a "__call__()" method in their class.\n\nModules\n   Modules are a basic organizational unit of Python code, and are\n   created by the *import system* as invoked either by the "import"\n   statement (see "import"), or by calling functions such as\n   "importlib.import_module()" and built-in "__import__()".  A module\n   object has a namespace implemented by a dictionary object (this is\n   the dictionary referenced by the "__globals__" attribute of\n   functions defined in the module).  Attribute references are\n   translated to lookups in this dictionary, e.g., "m.x" is equivalent\n   to "m.__dict__["x"]". A module object does not contain the code\n   object used to initialize the module (since it isn\'t needed once\n   the initialization is done).\n\n   Attribute assignment updates the module\'s namespace dictionary,\n   e.g., "m.x = 1" is equivalent to "m.__dict__["x"] = 1".\n\n   Special read-only attribute: "__dict__" is the module\'s namespace\n   as a dictionary object.\n\n   **CPython implementation detail:** Because of the way CPython\n   clears module dictionaries, the module dictionary will be cleared\n   when the module falls out of scope even if the dictionary still has\n   live references.  To avoid this, copy the dictionary or keep the\n   module around while using its dictionary directly.\n\n   Predefined (writable) attributes: "__name__" is the module\'s name;\n   "__doc__" is the module\'s documentation string, or "None" if\n   unavailable; "__file__" is the pathname of the file from which the\n   module was loaded, if it was loaded from a file. The "__file__"\n   attribute may be missing for certain types of modules, such as C\n   modules that are statically linked into the interpreter; for\n   extension modules loaded dynamically from a shared library, it is\n   the pathname of the shared library file.\n\nCustom classes\n   Custom class types are typically created by class definitions (see\n   section *Class definitions*).  A class has a namespace implemented\n   by a dictionary object. Class attribute references are translated\n   to lookups in this dictionary, e.g., "C.x" is translated to\n   "C.__dict__["x"]" (although there are a number of hooks which allow\n   for other means of locating attributes). When the attribute name is\n   not found there, the attribute search continues in the base\n   classes. This search of the base classes uses the C3 method\n   resolution order which behaves correctly even in the presence of\n   \'diamond\' inheritance structures where there are multiple\n   inheritance paths leading back to a common ancestor. Additional\n   details on the C3 MRO used by Python can be found in the\n   documentation accompanying the 2.3 release at\n   https://www.python.org/download/releases/2.3/mro/.\n\n   When a class attribute reference (for class "C", say) would yield a\n   class method object, it is transformed into an instance method\n   object whose "__self__" attributes is "C".  When it would yield a\n   static method object, it is transformed into the object wrapped by\n   the static method object. See section *Implementing Descriptors*\n   for another way in which attributes retrieved from a class may\n   differ from those actually contained in its "__dict__".\n\n   Class attribute assignments update the class\'s dictionary, never\n   the dictionary of a base class.\n\n   A class object can be called (see above) to yield a class instance\n   (see below).\n\n   Special attributes: "__name__" is the class name; "__module__" is\n   the module name in which the class was defined; "__dict__" is the\n   dictionary containing the class\'s namespace; "__bases__" is a tuple\n   (possibly empty or a singleton) containing the base classes, in the\n   order of their occurrence in the base class list; "__doc__" is the\n   class\'s documentation string, or None if undefined.\n\nClass instances\n   A class instance is created by calling a class object (see above).\n   A class instance has a namespace implemented as a dictionary which\n   is the first place in which attribute references are searched.\n   When an attribute is not found there, and the instance\'s class has\n   an attribute by that name, the search continues with the class\n   attributes.  If a class attribute is found that is a user-defined\n   function object, it is transformed into an instance method object\n   whose "__self__" attribute is the instance.  Static method and\n   class method objects are also transformed; see above under\n   "Classes".  See section *Implementing Descriptors* for another way\n   in which attributes of a class retrieved via its instances may\n   differ from the objects actually stored in the class\'s "__dict__".\n   If no class attribute is found, and the object\'s class has a\n   "__getattr__()" method, that is called to satisfy the lookup.\n\n   Attribute assignments and deletions update the instance\'s\n   dictionary, never a class\'s dictionary.  If the class has a\n   "__setattr__()" or "__delattr__()" method, this is called instead\n   of updating the instance dictionary directly.\n\n   Class instances can pretend to be numbers, sequences, or mappings\n   if they have methods with certain special names.  See section\n   *Special method names*.\n\n   Special attributes: "__dict__" is the attribute dictionary;\n   "__class__" is the instance\'s class.\n\nI/O objects (also known as file objects)\n   A *file object* represents an open file.  Various shortcuts are\n   available to create file objects: the "open()" built-in function,\n   and also "os.popen()", "os.fdopen()", and the "makefile()" method\n   of socket objects (and perhaps by other functions or methods\n   provided by extension modules).\n\n   The objects "sys.stdin", "sys.stdout" and "sys.stderr" are\n   initialized to file objects corresponding to the interpreter\'s\n   standard input, output and error streams; they are all open in text\n   mode and therefore follow the interface defined by the\n   "io.TextIOBase" abstract class.\n\nInternal types\n   A few types used internally by the interpreter are exposed to the\n   user. Their definitions may change with future versions of the\n   interpreter, but they are mentioned here for completeness.\n\n   Code objects\n      Code objects represent *byte-compiled* executable Python code,\n      or *bytecode*. The difference between a code object and a\n      function object is that the function object contains an explicit\n      reference to the function\'s globals (the module in which it was\n      defined), while a code object contains no context; also the\n      default argument values are stored in the function object, not\n      in the code object (because they represent values calculated at\n      run-time).  Unlike function objects, code objects are immutable\n      and contain no references (directly or indirectly) to mutable\n      objects.\n\n      Special read-only attributes: "co_name" gives the function name;\n      "co_argcount" is the number of positional arguments (including\n      arguments with default values); "co_nlocals" is the number of\n      local variables used by the function (including arguments);\n      "co_varnames" is a tuple containing the names of the local\n      variables (starting with the argument names); "co_cellvars" is a\n      tuple containing the names of local variables that are\n      referenced by nested functions; "co_freevars" is a tuple\n      containing the names of free variables; "co_code" is a string\n      representing the sequence of bytecode instructions; "co_consts"\n      is a tuple containing the literals used by the bytecode;\n      "co_names" is a tuple containing the names used by the bytecode;\n      "co_filename" is the filename from which the code was compiled;\n      "co_firstlineno" is the first line number of the function;\n      "co_lnotab" is a string encoding the mapping from bytecode\n      offsets to line numbers (for details see the source code of the\n      interpreter); "co_stacksize" is the required stack size\n      (including local variables); "co_flags" is an integer encoding a\n      number of flags for the interpreter.\n\n      The following flag bits are defined for "co_flags": bit "0x04"\n      is set if the function uses the "*arguments" syntax to accept an\n      arbitrary number of positional arguments; bit "0x08" is set if\n      the function uses the "**keywords" syntax to accept arbitrary\n      keyword arguments; bit "0x20" is set if the function is a\n      generator.\n\n      Future feature declarations ("from __future__ import division")\n      also use bits in "co_flags" to indicate whether a code object\n      was compiled with a particular feature enabled: bit "0x2000" is\n      set if the function was compiled with future division enabled;\n      bits "0x10" and "0x1000" were used in earlier versions of\n      Python.\n\n      Other bits in "co_flags" are reserved for internal use.\n\n      If a code object represents a function, the first item in\n      "co_consts" is the documentation string of the function, or\n      "None" if undefined.\n\n   Frame objects\n      Frame objects represent execution frames.  They may occur in\n      traceback objects (see below).\n\n      Special read-only attributes: "f_back" is to the previous stack\n      frame (towards the caller), or "None" if this is the bottom\n      stack frame; "f_code" is the code object being executed in this\n      frame; "f_locals" is the dictionary used to look up local\n      variables; "f_globals" is used for global variables;\n      "f_builtins" is used for built-in (intrinsic) names; "f_lasti"\n      gives the precise instruction (this is an index into the\n      bytecode string of the code object).\n\n      Special writable attributes: "f_trace", if not "None", is a\n      function called at the start of each source code line (this is\n      used by the debugger); "f_lineno" is the current line number of\n      the frame --- writing to this from within a trace function jumps\n      to the given line (only for the bottom-most frame).  A debugger\n      can implement a Jump command (aka Set Next Statement) by writing\n      to f_lineno.\n\n      Frame objects support one method:\n\n      frame.clear()\n\n         This method clears all references to local variables held by\n         the frame.  Also, if the frame belonged to a generator, the\n         generator is finalized.  This helps break reference cycles\n         involving frame objects (for example when catching an\n         exception and storing its traceback for later use).\n\n         "RuntimeError" is raised if the frame is currently executing.\n\n         New in version 3.4.\n\n   Traceback objects\n      Traceback objects represent a stack trace of an exception.  A\n      traceback object is created when an exception occurs.  When the\n      search for an exception handler unwinds the execution stack, at\n      each unwound level a traceback object is inserted in front of\n      the current traceback.  When an exception handler is entered,\n      the stack trace is made available to the program. (See section\n      *The try statement*.) It is accessible as the third item of the\n      tuple returned by "sys.exc_info()". When the program contains no\n      suitable handler, the stack trace is written (nicely formatted)\n      to the standard error stream; if the interpreter is interactive,\n      it is also made available to the user as "sys.last_traceback".\n\n      Special read-only attributes: "tb_next" is the next level in the\n      stack trace (towards the frame where the exception occurred), or\n      "None" if there is no next level; "tb_frame" points to the\n      execution frame of the current level; "tb_lineno" gives the line\n      number where the exception occurred; "tb_lasti" indicates the\n      precise instruction.  The line number and last instruction in\n      the traceback may differ from the line number of its frame\n      object if the exception occurred in a "try" statement with no\n      matching except clause or with a finally clause.\n\n   Slice objects\n      Slice objects are used to represent slices for "__getitem__()"\n      methods.  They are also created by the built-in "slice()"\n      function.\n\n      Special read-only attributes: "start" is the lower bound; "stop"\n      is the upper bound; "step" is the step value; each is "None" if\n      omitted.  These attributes can have any type.\n\n      Slice objects support one method:\n\n      slice.indices(self, length)\n\n         This method takes a single integer argument *length* and\n         computes information about the slice that the slice object\n         would describe if applied to a sequence of *length* items.\n         It returns a tuple of three integers; respectively these are\n         the *start* and *stop* indices and the *step* or stride\n         length of the slice. Missing or out-of-bounds indices are\n         handled in a manner consistent with regular slices.\n\n   Static method objects\n      Static method objects provide a way of defeating the\n      transformation of function objects to method objects described\n      above. A static method object is a wrapper around any other\n      object, usually a user-defined method object. When a static\n      method object is retrieved from a class or a class instance, the\n      object actually returned is the wrapped object, which is not\n      subject to any further transformation. Static method objects are\n      not themselves callable, although the objects they wrap usually\n      are. Static method objects are created by the built-in\n      "staticmethod()" constructor.\n\n   Class method objects\n      A class method object, like a static method object, is a wrapper\n      around another object that alters the way in which that object\n      is retrieved from classes and class instances. The behaviour of\n      class method objects upon such retrieval is described above,\n      under "User-defined methods". Class method objects are created\n      by the built-in "classmethod()" constructor.\n',
  'typesfunctions': u'\nFunctions\n*********\n\nFunction objects are created by function definitions.  The only\noperation on a function object is to call it: "func(argument-list)".\n\nThere are really two flavors of function objects: built-in functions\nand user-defined functions.  Both support the same operation (to call\nthe function), but the implementation is different, hence the\ndifferent object types.\n\nSee *Function definitions* for more information.\n',
- 'typesmapping': u'\nMapping Types --- "dict"\n************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects.  There is currently only one standard\nmapping type, the *dictionary*.  (For other containers see the built-\nin "list", "set", and "tuple" classes, and the "collections" module.)\n\nA dictionary\'s keys are *almost* arbitrary values.  Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys.  Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as "1" and "1.0") then they can be used interchangeably to index\nthe same dictionary entry.  (Note however, that since computers store\nfloating-point numbers as approximations it is usually unwise to use\nthem as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of "key:\nvalue" pairs within braces, for example: "{\'jack\': 4098, \'sjoerd\':\n4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the "dict"\nconstructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n   Return a new dictionary initialized from an optional positional\n   argument and a possibly empty set of keyword arguments.\n\n   If no positional argument is given, an empty dictionary is created.\n   If a positional argument is given and it is a mapping object, a\n   dictionary is created with the same key-value pairs as the mapping\n   object.  Otherwise, the positional argument must be an *iterable*\n   object.  Each item in the iterable must itself be an iterable with\n   exactly two objects.  The first object of each item becomes a key\n   in the new dictionary, and the second object the corresponding\n   value.  If a key occurs more than once, the last value for that key\n   becomes the corresponding value in the new dictionary.\n\n   If keyword arguments are given, the keyword arguments and their\n   values are added to the dictionary created from the positional\n   argument.  If a key being added is already present, the value from\n   the keyword argument replaces the value from the positional\n   argument.\n\n   To illustrate, the following examples all return a dictionary equal\n   to "{"one": 1, "two": 2, "three": 3}":\n\n      >>> a = dict(one=1, two=2, three=3)\n      >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n      >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n      >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n      >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n      >>> a == b == c == d == e\n      True\n\n   Providing keyword arguments as in the first example only works for\n   keys that are valid Python identifiers.  Otherwise, any valid keys\n   can be used.\n\n   These are the operations that dictionaries support (and therefore,\n   custom mapping types should support too):\n\n   len(d)\n\n      Return the number of items in the dictionary *d*.\n\n   d[key]\n\n      Return the item of *d* with key *key*.  Raises a "KeyError" if\n      *key* is not in the map.\n\n      If a subclass of dict defines a method "__missing__()" and *key*\n      is not present, the "d[key]" operation calls that method with\n      the key *key* as argument.  The "d[key]" operation then returns\n      or raises whatever is returned or raised by the\n      "__missing__(key)" call. No other operations or methods invoke\n      "__missing__()". If "__missing__()" is not defined, "KeyError"\n      is raised. "__missing__()" must be a method; it cannot be an\n      instance variable:\n\n         >>> class Counter(dict):\n         ...     def __missing__(self, key):\n         ...         return 0\n         >>> c = Counter()\n         >>> c[\'red\']\n         0\n         >>> c[\'red\'] += 1\n         >>> c[\'red\']\n         1\n\n      The example above shows part of the implementation of\n      "collections.Counter".  A different "__missing__" method is used\n      by "collections.defaultdict".\n\n   d[key] = value\n\n      Set "d[key]" to *value*.\n\n   del d[key]\n\n      Remove "d[key]" from *d*.  Raises a "KeyError" if *key* is not\n      in the map.\n\n   key in d\n\n      Return "True" if *d* has a key *key*, else "False".\n\n   key not in d\n\n      Equivalent to "not key in d".\n\n   iter(d)\n\n      Return an iterator over the keys of the dictionary.  This is a\n      shortcut for "iter(d.keys())".\n\n   clear()\n\n      Remove all items from the dictionary.\n\n   copy()\n\n      Return a shallow copy of the dictionary.\n\n   classmethod fromkeys(seq[, value])\n\n      Create a new dictionary with keys from *seq* and values set to\n      *value*.\n\n      "fromkeys()" is a class method that returns a new dictionary.\n      *value* defaults to "None".\n\n   get(key[, default])\n\n      Return the value for *key* if *key* is in the dictionary, else\n      *default*. If *default* is not given, it defaults to "None", so\n      that this method never raises a "KeyError".\n\n   items()\n\n      Return a new view of the dictionary\'s items ("(key, value)"\n      pairs). See the *documentation of view objects*.\n\n   keys()\n\n      Return a new view of the dictionary\'s keys.  See the\n      *documentation of view objects*.\n\n   pop(key[, default])\n\n      If *key* is in the dictionary, remove it and return its value,\n      else return *default*.  If *default* is not given and *key* is\n      not in the dictionary, a "KeyError" is raised.\n\n   popitem()\n\n      Remove and return an arbitrary "(key, value)" pair from the\n      dictionary.\n\n      "popitem()" is useful to destructively iterate over a\n      dictionary, as often used in set algorithms.  If the dictionary\n      is empty, calling "popitem()" raises a "KeyError".\n\n   setdefault(key[, default])\n\n      If *key* is in the dictionary, return its value.  If not, insert\n      *key* with a value of *default* and return *default*.  *default*\n      defaults to "None".\n\n   update([other])\n\n      Update the dictionary with the key/value pairs from *other*,\n      overwriting existing keys.  Return "None".\n\n      "update()" accepts either another dictionary object or an\n      iterable of key/value pairs (as tuples or other iterables of\n      length two).  If keyword arguments are specified, the dictionary\n      is then updated with those key/value pairs: "d.update(red=1,\n      blue=2)".\n\n   values()\n\n      Return a new view of the dictionary\'s values.  See the\n      *documentation of view objects*.\n\nSee also: "types.MappingProxyType" can be used to create a read-only\n  view of a "dict".\n\n\nDictionary view objects\n=======================\n\nThe objects returned by "dict.keys()", "dict.values()" and\n"dict.items()" are *view objects*.  They provide a dynamic view on the\ndictionary\'s entries, which means that when the dictionary changes,\nthe view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n   Return the number of entries in the dictionary.\n\niter(dictview)\n\n   Return an iterator over the keys, values or items (represented as\n   tuples of "(key, value)") in the dictionary.\n\n   Keys and values are iterated over in an arbitrary order which is\n   non-random, varies across Python implementations, and depends on\n   the dictionary\'s history of insertions and deletions. If keys,\n   values and items views are iterated over with no intervening\n   modifications to the dictionary, the order of items will directly\n   correspond.  This allows the creation of "(value, key)" pairs using\n   "zip()": "pairs = zip(d.values(), d.keys())".  Another way to\n   create the same list is "pairs = [(v, k) for (k, v) in d.items()]".\n\n   Iterating views while adding or deleting entries in the dictionary\n   may raise a "RuntimeError" or fail to iterate over all entries.\n\nx in dictview\n\n   Return "True" if *x* is in the underlying dictionary\'s keys, values\n   or items (in the latter case, *x* should be a "(key, value)"\n   tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that "(key, value)" pairs are unique\nand hashable, then the items view is also set-like.  (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class "collections.abc.Set" are available (for example, "==",\n"<", or "^").\n\nAn example of dictionary view usage:\n\n   >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n   >>> keys = dishes.keys()\n   >>> values = dishes.values()\n\n   >>> # iteration\n   >>> n = 0\n   >>> for val in values:\n   ...     n += val\n   >>> print(n)\n   504\n\n   >>> # keys and values are iterated over in the same order\n   >>> list(keys)\n   [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n   >>> list(values)\n   [2, 1, 1, 500]\n\n   >>> # view objects are dynamic and reflect dict changes\n   >>> del dishes[\'eggs\']\n   >>> del dishes[\'sausage\']\n   >>> list(keys)\n   [\'spam\', \'bacon\']\n\n   >>> # set operations\n   >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n   {\'bacon\'}\n   >>> keys ^ {\'sausage\', \'juice\'}\n   {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n',
+ 'typesmapping': u'\nMapping Types --- "dict"\n************************\n\nA *mapping* object maps *hashable* values to arbitrary objects.\nMappings are mutable objects.  There is currently only one standard\nmapping type, the *dictionary*.  (For other containers see the built-\nin "list", "set", and "tuple" classes, and the "collections" module.)\n\nA dictionary\'s keys are *almost* arbitrary values.  Values that are\nnot *hashable*, that is, values containing lists, dictionaries or\nother mutable types (that are compared by value rather than by object\nidentity) may not be used as keys.  Numeric types used for keys obey\nthe normal rules for numeric comparison: if two numbers compare equal\n(such as "1" and "1.0") then they can be used interchangeably to index\nthe same dictionary entry.  (Note however, that since computers store\nfloating-point numbers as approximations it is usually unwise to use\nthem as dictionary keys.)\n\nDictionaries can be created by placing a comma-separated list of "key:\nvalue" pairs within braces, for example: "{\'jack\': 4098, \'sjoerd\':\n4127}" or "{4098: \'jack\', 4127: \'sjoerd\'}", or by the "dict"\nconstructor.\n\nclass class dict(**kwarg)\nclass class dict(mapping, **kwarg)\nclass class dict(iterable, **kwarg)\n\n   Return a new dictionary initialized from an optional positional\n   argument and a possibly empty set of keyword arguments.\n\n   If no positional argument is given, an empty dictionary is created.\n   If a positional argument is given and it is a mapping object, a\n   dictionary is created with the same key-value pairs as the mapping\n   object.  Otherwise, the positional argument must be an *iterable*\n   object.  Each item in the iterable must itself be an iterable with\n   exactly two objects.  The first object of each item becomes a key\n   in the new dictionary, and the second object the corresponding\n   value.  If a key occurs more than once, the last value for that key\n   becomes the corresponding value in the new dictionary.\n\n   If keyword arguments are given, the keyword arguments and their\n   values are added to the dictionary created from the positional\n   argument.  If a key being added is already present, the value from\n   the keyword argument replaces the value from the positional\n   argument.\n\n   To illustrate, the following examples all return a dictionary equal\n   to "{"one": 1, "two": 2, "three": 3}":\n\n      >>> a = dict(one=1, two=2, three=3)\n      >>> b = {\'one\': 1, \'two\': 2, \'three\': 3}\n      >>> c = dict(zip([\'one\', \'two\', \'three\'], [1, 2, 3]))\n      >>> d = dict([(\'two\', 2), (\'one\', 1), (\'three\', 3)])\n      >>> e = dict({\'three\': 3, \'one\': 1, \'two\': 2})\n      >>> a == b == c == d == e\n      True\n\n   Providing keyword arguments as in the first example only works for\n   keys that are valid Python identifiers.  Otherwise, any valid keys\n   can be used.\n\n   These are the operations that dictionaries support (and therefore,\n   custom mapping types should support too):\n\n   len(d)\n\n      Return the number of items in the dictionary *d*.\n\n   d[key]\n\n      Return the item of *d* with key *key*.  Raises a "KeyError" if\n      *key* is not in the map.\n\n      If a subclass of dict defines a method "__missing__()" and *key*\n      is not present, the "d[key]" operation calls that method with\n      the key *key* as argument.  The "d[key]" operation then returns\n      or raises whatever is returned or raised by the\n      "__missing__(key)" call. No other operations or methods invoke\n      "__missing__()". If "__missing__()" is not defined, "KeyError"\n      is raised. "__missing__()" must be a method; it cannot be an\n      instance variable:\n\n         >>> class Counter(dict):\n         ...     def __missing__(self, key):\n         ...         return 0\n         >>> c = Counter()\n         >>> c[\'red\']\n         0\n         >>> c[\'red\'] += 1\n         >>> c[\'red\']\n         1\n\n      The example above shows part of the implementation of\n      "collections.Counter".  A different "__missing__" method is used\n      by "collections.defaultdict".\n\n   d[key] = value\n\n      Set "d[key]" to *value*.\n\n   del d[key]\n\n      Remove "d[key]" from *d*.  Raises a "KeyError" if *key* is not\n      in the map.\n\n   key in d\n\n      Return "True" if *d* has a key *key*, else "False".\n\n   key not in d\n\n      Equivalent to "not key in d".\n\n   iter(d)\n\n      Return an iterator over the keys of the dictionary.  This is a\n      shortcut for "iter(d.keys())".\n\n   clear()\n\n      Remove all items from the dictionary.\n\n   copy()\n\n      Return a shallow copy of the dictionary.\n\n   classmethod fromkeys(seq[, value])\n\n      Create a new dictionary with keys from *seq* and values set to\n      *value*.\n\n      "fromkeys()" is a class method that returns a new dictionary.\n      *value* defaults to "None".\n\n   get(key[, default])\n\n      Return the value for *key* if *key* is in the dictionary, else\n      *default*. If *default* is not given, it defaults to "None", so\n      that this method never raises a "KeyError".\n\n   items()\n\n      Return a new view of the dictionary\'s items ("(key, value)"\n      pairs). See the *documentation of view objects*.\n\n   keys()\n\n      Return a new view of the dictionary\'s keys.  See the\n      *documentation of view objects*.\n\n   pop(key[, default])\n\n      If *key* is in the dictionary, remove it and return its value,\n      else return *default*.  If *default* is not given and *key* is\n      not in the dictionary, a "KeyError" is raised.\n\n   popitem()\n\n      Remove and return an arbitrary "(key, value)" pair from the\n      dictionary.\n\n      "popitem()" is useful to destructively iterate over a\n      dictionary, as often used in set algorithms.  If the dictionary\n      is empty, calling "popitem()" raises a "KeyError".\n\n   setdefault(key[, default])\n\n      If *key* is in the dictionary, return its value.  If not, insert\n      *key* with a value of *default* and return *default*.  *default*\n      defaults to "None".\n\n   update([other])\n\n      Update the dictionary with the key/value pairs from *other*,\n      overwriting existing keys.  Return "None".\n\n      "update()" accepts either another dictionary object or an\n      iterable of key/value pairs (as tuples or other iterables of\n      length two).  If keyword arguments are specified, the dictionary\n      is then updated with those key/value pairs: "d.update(red=1,\n      blue=2)".\n\n   values()\n\n      Return a new view of the dictionary\'s values.  See the\n      *documentation of view objects*.\n\n   Dictionaries compare equal if and only if they have the same "(key,\n   value)" pairs. Order comparisons (\'<\', \'<=\', \'>=\', \'>\') raise\n   "TypeError".\n\nSee also: "types.MappingProxyType" can be used to create a read-only\n  view of a "dict".\n\n\nDictionary view objects\n=======================\n\nThe objects returned by "dict.keys()", "dict.values()" and\n"dict.items()" are *view objects*.  They provide a dynamic view on the\ndictionary\'s entries, which means that when the dictionary changes,\nthe view reflects these changes.\n\nDictionary views can be iterated over to yield their respective data,\nand support membership tests:\n\nlen(dictview)\n\n   Return the number of entries in the dictionary.\n\niter(dictview)\n\n   Return an iterator over the keys, values or items (represented as\n   tuples of "(key, value)") in the dictionary.\n\n   Keys and values are iterated over in an arbitrary order which is\n   non-random, varies across Python implementations, and depends on\n   the dictionary\'s history of insertions and deletions. If keys,\n   values and items views are iterated over with no intervening\n   modifications to the dictionary, the order of items will directly\n   correspond.  This allows the creation of "(value, key)" pairs using\n   "zip()": "pairs = zip(d.values(), d.keys())".  Another way to\n   create the same list is "pairs = [(v, k) for (k, v) in d.items()]".\n\n   Iterating views while adding or deleting entries in the dictionary\n   may raise a "RuntimeError" or fail to iterate over all entries.\n\nx in dictview\n\n   Return "True" if *x* is in the underlying dictionary\'s keys, values\n   or items (in the latter case, *x* should be a "(key, value)"\n   tuple).\n\nKeys views are set-like since their entries are unique and hashable.\nIf all values are hashable, so that "(key, value)" pairs are unique\nand hashable, then the items view is also set-like.  (Values views are\nnot treated as set-like since the entries are generally not unique.)\nFor set-like views, all of the operations defined for the abstract\nbase class "collections.abc.Set" are available (for example, "==",\n"<", or "^").\n\nAn example of dictionary view usage:\n\n   >>> dishes = {\'eggs\': 2, \'sausage\': 1, \'bacon\': 1, \'spam\': 500}\n   >>> keys = dishes.keys()\n   >>> values = dishes.values()\n\n   >>> # iteration\n   >>> n = 0\n   >>> for val in values:\n   ...     n += val\n   >>> print(n)\n   504\n\n   >>> # keys and values are iterated over in the same order\n   >>> list(keys)\n   [\'eggs\', \'bacon\', \'sausage\', \'spam\']\n   >>> list(values)\n   [2, 1, 1, 500]\n\n   >>> # view objects are dynamic and reflect dict changes\n   >>> del dishes[\'eggs\']\n   >>> del dishes[\'sausage\']\n   >>> list(keys)\n   [\'spam\', \'bacon\']\n\n   >>> # set operations\n   >>> keys & {\'eggs\', \'bacon\', \'salad\'}\n   {\'bacon\'}\n   >>> keys ^ {\'sausage\', \'juice\'}\n   {\'juice\', \'sausage\', \'bacon\', \'spam\'}\n',
  'typesmethods': u'\nMethods\n*******\n\nMethods are functions that are called using the attribute notation.\nThere are two flavors: built-in methods (such as "append()" on lists)\nand class instance methods.  Built-in methods are described with the\ntypes that support them.\n\nIf you access a method (a function defined in a class namespace)\nthrough an instance, you get a special object: a *bound method* (also\ncalled *instance method*) object. When called, it will add the "self"\nargument to the argument list.  Bound methods have two special read-\nonly attributes: "m.__self__" is the object on which the method\noperates, and "m.__func__" is the function implementing the method.\nCalling "m(arg-1, arg-2, ..., arg-n)" is completely equivalent to\ncalling "m.__func__(m.__self__, arg-1, arg-2, ..., arg-n)".\n\nLike function objects, bound method objects support getting arbitrary\nattributes.  However, since method attributes are actually stored on\nthe underlying function object ("meth.__func__"), setting method\nattributes on bound methods is disallowed.  Attempting to set an\nattribute on a method results in an "AttributeError" being raised.  In\norder to set a method attribute, you need to explicitly set it on the\nunderlying function object:\n\n   >>> class C:\n   ...     def method(self):\n   ...         pass\n   ...\n   >>> c = C()\n   >>> c.method.whoami = \'my name is method\'  # can\'t set on the method\n   Traceback (most recent call last):\n     File "<stdin>", line 1, in <module>\n   AttributeError: \'method\' object has no attribute \'whoami\'\n   >>> c.method.__func__.whoami = \'my name is method\'\n   >>> c.method.whoami\n   \'my name is method\'\n\nSee *The standard type hierarchy* for more information.\n',
  'typesmodules': u'\nModules\n*******\n\nThe only special operation on a module is attribute access: "m.name",\nwhere *m* is a module and *name* accesses a name defined in *m*\'s\nsymbol table. Module attributes can be assigned to.  (Note that the\n"import" statement is not, strictly speaking, an operation on a module\nobject; "import foo" does not require a module object named *foo* to\nexist, rather it requires an (external) *definition* for a module\nnamed *foo* somewhere.)\n\nA special attribute of every module is "__dict__". This is the\ndictionary containing the module\'s symbol table. Modifying this\ndictionary will actually change the module\'s symbol table, but direct\nassignment to the "__dict__" attribute is not possible (you can write\n"m.__dict__[\'a\'] = 1", which defines "m.a" to be "1", but you can\'t\nwrite "m.__dict__ = {}").  Modifying "__dict__" directly is not\nrecommended.\n\nModules built into the interpreter are written like this: "<module\n\'sys\' (built-in)>".  If loaded from a file, they are written as\n"<module \'os\' from \'/usr/local/lib/pythonX.Y/os.pyc\'>".\n',
  'typesseq': u'\nSequence Types --- "list", "tuple", "range"\n*******************************************\n\nThere are three basic sequence types: lists, tuples, and range\nobjects. Additional sequence types tailored for processing of *binary\ndata* and *text strings* are described in dedicated sections.\n\n\nCommon Sequence Operations\n==========================\n\nThe operations in the following table are supported by most sequence\ntypes, both mutable and immutable. The "collections.abc.Sequence" ABC\nis provided to make it easier to correctly implement these operations\non custom sequence types.\n\nThis table lists the sequence operations sorted in ascending priority.\nIn the table, *s* and *t* are sequences of the same type, *n*, *i*,\n*j* and *k* are integers and *x* is an arbitrary object that meets any\ntype and value restrictions imposed by *s*.\n\nThe "in" and "not in" operations have the same priorities as the\ncomparison operations. The "+" (concatenation) and "*" (repetition)\noperations have the same priority as the corresponding numeric\noperations.\n\n+----------------------------+----------------------------------+------------+\n| Operation                  | Result                           | Notes      |\n+============================+==================================+============+\n| "x in s"                   | "True" if an item of *s* is      | (1)        |\n|                            | equal to *x*, else "False"       |            |\n+----------------------------+----------------------------------+------------+\n| "x not in s"               | "False" if an item of *s* is     | (1)        |\n|                            | equal to *x*, else "True"        |            |\n+----------------------------+----------------------------------+------------+\n| "s + t"                    | the concatenation of *s* and *t* | (6)(7)     |\n+----------------------------+----------------------------------+------------+\n| "s * n" or "n * s"         | *n* shallow copies of *s*        | (2)(7)     |\n|                            | concatenated                     |            |\n+----------------------------+----------------------------------+------------+\n| "s[i]"                     | *i*th item of *s*, origin 0      | (3)        |\n+----------------------------+----------------------------------+------------+\n| "s[i:j]"                   | slice of *s* from *i* to *j*     | (3)(4)     |\n+----------------------------+----------------------------------+------------+\n| "s[i:j:k]"                 | slice of *s* from *i* to *j*     | (3)(5)     |\n|                            | with step *k*                    |            |\n+----------------------------+----------------------------------+------------+\n| "len(s)"                   | length of *s*                    |            |\n+----------------------------+----------------------------------+------------+\n| "min(s)"                   | smallest item of *s*             |            |\n+----------------------------+----------------------------------+------------+\n| "max(s)"                   | largest item of *s*              |            |\n+----------------------------+----------------------------------+------------+\n| "s.index(x[, i[, j]])"     | index of the first occurrence of | (8)        |\n|                            | *x* in *s* (at or after index    |            |\n|                            | *i* and before index *j*)        |            |\n+----------------------------+----------------------------------+------------+\n| "s.count(x)"               | total number of occurrences of   |            |\n|                            | *x* in *s*                       |            |\n+----------------------------+----------------------------------+------------+\n\nSequences of the same type also support comparisons.  In particular,\ntuples and lists are compared lexicographically by comparing\ncorresponding elements. This means that to compare equal, every\nelement must compare equal and the two sequences must be of the same\ntype and have the same length.  (For full details see *Comparisons* in\nthe language reference.)\n\nNotes:\n\n1. While the "in" and "not in" operations are used only for simple\n   containment testing in the general case, some specialised sequences\n   (such as "str", "bytes" and "bytearray") also use them for\n   subsequence testing:\n\n      >>> "gg" in "eggs"\n      True\n\n2. Values of *n* less than "0" are treated as "0" (which yields an\n   empty sequence of the same type as *s*).  Note also that the copies\n   are shallow; nested structures are not copied.  This often haunts\n   new Python programmers; consider:\n\n      >>> lists = [[]] * 3\n      >>> lists\n      [[], [], []]\n      >>> lists[0].append(3)\n      >>> lists\n      [[3], [3], [3]]\n\n   What has happened is that "[[]]" is a one-element list containing\n   an empty list, so all three elements of "[[]] * 3" are (pointers\n   to) this single empty list.  Modifying any of the elements of\n   "lists" modifies this single list. You can create a list of\n   different lists this way:\n\n      >>> lists = [[] for i in range(3)]\n      >>> lists[0].append(3)\n      >>> lists[1].append(5)\n      >>> lists[2].append(7)\n      >>> lists\n      [[3], [5], [7]]\n\n3. If *i* or *j* is negative, the index is relative to the end of\n   the string: "len(s) + i" or "len(s) + j" is substituted.  But note\n   that "-0" is still "0".\n\n4. The slice of *s* from *i* to *j* is defined as the sequence of\n   items with index *k* such that "i <= k < j".  If *i* or *j* is\n   greater than "len(s)", use "len(s)".  If *i* is omitted or "None",\n   use "0".  If *j* is omitted or "None", use "len(s)".  If *i* is\n   greater than or equal to *j*, the slice is empty.\n\n5. The slice of *s* from *i* to *j* with step *k* is defined as the\n   sequence of items with index  "x = i + n*k" such that "0 <= n <\n   (j-i)/k".  In other words, the indices are "i", "i+k", "i+2*k",\n   "i+3*k" and so on, stopping when *j* is reached (but never\n   including *j*).  If *i* or *j* is greater than "len(s)", use\n   "len(s)".  If *i* or *j* are omitted or "None", they become "end"\n   values (which end depends on the sign of *k*).  Note, *k* cannot be\n   zero. If *k* is "None", it is treated like "1".\n\n6. Concatenating immutable sequences always results in a new\n   object. This means that building up a sequence by repeated\n   concatenation will have a quadratic runtime cost in the total\n   sequence length. To get a linear runtime cost, you must switch to\n   one of the alternatives below:\n\n   * if concatenating "str" objects, you can build a list and use\n     "str.join()" at the end or else write to a "io.StringIO" instance\n     and retrieve its value when complete\n\n   * if concatenating "bytes" objects, you can similarly use\n     "bytes.join()" or "io.BytesIO", or you can do in-place\n     concatenation with a "bytearray" object.  "bytearray" objects are\n     mutable and have an efficient overallocation mechanism\n\n   * if concatenating "tuple" objects, extend a "list" instead\n\n   * for other types, investigate the relevant class documentation\n\n7. Some sequence types (such as "range") only support item\n   sequences that follow specific patterns, and hence don\'t support\n   sequence concatenation or repetition.\n\n8. "index" raises "ValueError" when *x* is not found in *s*. When\n   supported, the additional arguments to the index method allow\n   efficient searching of subsections of the sequence. Passing the\n   extra arguments is roughly equivalent to using "s[i:j].index(x)",\n   only without copying any data and with the returned index being\n   relative to the start of the sequence rather than the start of the\n   slice.\n\n\nImmutable Sequence Types\n========================\n\nThe only operation that immutable sequence types generally implement\nthat is not also implemented by mutable sequence types is support for\nthe "hash()" built-in.\n\nThis support allows immutable sequences, such as "tuple" instances, to\nbe used as "dict" keys and stored in "set" and "frozenset" instances.\n\nAttempting to hash an immutable sequence that contains unhashable\nvalues will result in "TypeError".\n\n\nMutable Sequence Types\n======================\n\nThe operations in the following table are defined on mutable sequence\ntypes. The "collections.abc.MutableSequence" ABC is provided to make\nit easier to correctly implement these operations on custom sequence\ntypes.\n\nIn the table *s* is an instance of a mutable sequence type, *t* is any\niterable object and *x* is an arbitrary object that meets any type and\nvalue restrictions imposed by *s* (for example, "bytearray" only\naccepts integers that meet the value restriction "0 <= x <= 255").\n\n+--------------------------------+----------------------------------+-----------------------+\n| Operation                      | Result                           | Notes                 |\n+================================+==================================+=======================+\n| "s[i] = x"                     | item *i* of *s* is replaced by   |                       |\n|                                | *x*                              |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s[i:j] = t"                   | slice of *s* from *i* to *j* is  |                       |\n|                                | replaced by the contents of the  |                       |\n|                                | iterable *t*                     |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "del s[i:j]"                   | same as "s[i:j] = []"            |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s[i:j:k] = t"                 | the elements of "s[i:j:k]" are   | (1)                   |\n|                                | replaced by those of *t*         |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "del s[i:j:k]"                 | removes the elements of          |                       |\n|                                | "s[i:j:k]" from the list         |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.append(x)"                  | appends *x* to the end of the    |                       |\n|                                | sequence (same as                |                       |\n|                                | "s[len(s):len(s)] = [x]")        |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.clear()"                    | removes all items from "s" (same | (5)                   |\n|                                | as "del s[:]")                   |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.copy()"                     | creates a shallow copy of "s"    | (5)                   |\n|                                | (same as "s[:]")                 |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.extend(t)"                  | extends *s* with the contents of |                       |\n|                                | *t* (same as "s[len(s):len(s)] = |                       |\n|                                | t")                              |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.insert(i, x)"               | inserts *x* into *s* at the      |                       |\n|                                | index given by *i* (same as      |                       |\n|                                | "s[i:i] = [x]")                  |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.pop([i])"                   | retrieves the item at *i* and    | (2)                   |\n|                                | also removes it from *s*         |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.remove(x)"                  | remove the first item from *s*   | (3)                   |\n|                                | where "s[i] == x"                |                       |\n+--------------------------------+----------------------------------+-----------------------+\n| "s.reverse()"                  | reverses the items of *s* in     | (4)                   |\n|                                | place                            |                       |\n+--------------------------------+----------------------------------+-----------------------+\n\nNotes:\n\n1. *t* must have the same length as the slice it is replacing.\n\n2. The optional argument *i* defaults to "-1", so that by default\n   the last item is removed and returned.\n\n3. "remove" raises "ValueError" when *x* is not found in *s*.\n\n4. The "reverse()" method modifies the sequence in place for\n   economy of space when reversing a large sequence.  To remind users\n   that it operates by side effect, it does not return the reversed\n   sequence.\n\n5. "clear()" and "copy()" are included for consistency with the\n   interfaces of mutable containers that don\'t support slicing\n   operations (such as "dict" and "set")\n\n   New in version 3.3: "clear()" and "copy()" methods.\n\n\nLists\n=====\n\nLists are mutable sequences, typically used to store collections of\nhomogeneous items (where the precise degree of similarity will vary by\napplication).\n\nclass class list([iterable])\n\n   Lists may be constructed in several ways:\n\n   * Using a pair of square brackets to denote the empty list: "[]"\n\n   * Using square brackets, separating items with commas: "[a]",\n     "[a, b, c]"\n\n   * Using a list comprehension: "[x for x in iterable]"\n\n   * Using the type constructor: "list()" or "list(iterable)"\n\n   The constructor builds a list whose items are the same and in the\n   same order as *iterable*\'s items.  *iterable* may be either a\n   sequence, a container that supports iteration, or an iterator\n   object.  If *iterable* is already a list, a copy is made and\n   returned, similar to "iterable[:]". For example, "list(\'abc\')"\n   returns "[\'a\', \'b\', \'c\']" and "list( (1, 2, 3) )" returns "[1, 2,\n   3]". If no argument is given, the constructor creates a new empty\n   list, "[]".\n\n   Many other operations also produce lists, including the "sorted()"\n   built-in.\n\n   Lists implement all of the *common* and *mutable* sequence\n   operations. Lists also provide the following additional method:\n\n   sort(*, key=None, reverse=None)\n\n      This method sorts the list in place, using only "<" comparisons\n      between items. Exceptions are not suppressed - if any comparison\n      operations fail, the entire sort operation will fail (and the\n      list will likely be left in a partially modified state).\n\n      "sort()" accepts two arguments that can only be passed by\n      keyword (*keyword-only arguments*):\n\n      *key* specifies a function of one argument that is used to\n      extract a comparison key from each list element (for example,\n      "key=str.lower"). The key corresponding to each item in the list\n      is calculated once and then used for the entire sorting process.\n      The default value of "None" means that list items are sorted\n      directly without calculating a separate key value.\n\n      The "functools.cmp_to_key()" utility is available to convert a\n      2.x style *cmp* function to a *key* function.\n\n      *reverse* is a boolean value.  If set to "True", then the list\n      elements are sorted as if each comparison were reversed.\n\n      This method modifies the sequence in place for economy of space\n      when sorting a large sequence.  To remind users that it operates\n      by side effect, it does not return the sorted sequence (use\n      "sorted()" to explicitly request a new sorted list instance).\n\n      The "sort()" method is guaranteed to be stable.  A sort is\n      stable if it guarantees not to change the relative order of\n      elements that compare equal --- this is helpful for sorting in\n      multiple passes (for example, sort by department, then by salary\n      grade).\n\n      **CPython implementation detail:** While a list is being sorted,\n      the effect of attempting to mutate, or even inspect, the list is\n      undefined.  The C implementation of Python makes the list appear\n      empty for the duration, and raises "ValueError" if it can detect\n      that the list has been mutated during a sort.\n\n\nTuples\n======\n\nTuples are immutable sequences, typically used to store collections of\nheterogeneous data (such as the 2-tuples produced by the "enumerate()"\nbuilt-in). Tuples are also used for cases where an immutable sequence\nof homogeneous data is needed (such as allowing storage in a "set" or\n"dict" instance).\n\nclass class tuple([iterable])\n\n   Tuples may be constructed in a number of ways:\n\n   * Using a pair of parentheses to denote the empty tuple: "()"\n\n   * Using a trailing comma for a singleton tuple: "a," or "(a,)"\n\n   * Separating items with commas: "a, b, c" or "(a, b, c)"\n\n   * Using the "tuple()" built-in: "tuple()" or "tuple(iterable)"\n\n   The constructor builds a tuple whose items are the same and in the\n   same order as *iterable*\'s items.  *iterable* may be either a\n   sequence, a container that supports iteration, or an iterator\n   object.  If *iterable* is already a tuple, it is returned\n   unchanged. For example, "tuple(\'abc\')" returns "(\'a\', \'b\', \'c\')"\n   and "tuple( [1, 2, 3] )" returns "(1, 2, 3)". If no argument is\n   given, the constructor creates a new empty tuple, "()".\n\n   Note that it is actually the comma which makes a tuple, not the\n   parentheses. The parentheses are optional, except in the empty\n   tuple case, or when they are needed to avoid syntactic ambiguity.\n   For example, "f(a, b, c)" is a function call with three arguments,\n   while "f((a, b, c))" is a function call with a 3-tuple as the sole\n   argument.\n\n   Tuples implement all of the *common* sequence operations.\n\nFor heterogeneous collections of data where access by name is clearer\nthan access by index, "collections.namedtuple()" may be a more\nappropriate choice than a simple tuple object.\n\n\nRanges\n======\n\nThe "range" type represents an immutable sequence of numbers and is\ncommonly used for looping a specific number of times in "for" loops.\n\nclass class range(stop)\nclass class range(start, stop[, step])\n\n   The arguments to the range constructor must be integers (either\n   built-in "int" or any object that implements the "__index__"\n   special method).  If the *step* argument is omitted, it defaults to\n   "1". If the *start* argument is omitted, it defaults to "0". If\n   *step* is zero, "ValueError" is raised.\n\n   For a positive *step*, the contents of a range "r" are determined\n   by the formula "r[i] = start + step*i" where "i >= 0" and "r[i] <\n   stop".\n\n   For a negative *step*, the contents of the range are still\n   determined by the formula "r[i] = start + step*i", but the\n   constraints are "i >= 0" and "r[i] > stop".\n\n   A range object will be empty if "r[0]" does not meet the value\n   constraint. Ranges do support negative indices, but these are\n   interpreted as indexing from the end of the sequence determined by\n   the positive indices.\n\n   Ranges containing absolute values larger than "sys.maxsize" are\n   permitted but some features (such as "len()") may raise\n   "OverflowError".\n\n   Range examples:\n\n      >>> list(range(10))\n      [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n      >>> list(range(1, 11))\n      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n      >>> list(range(0, 30, 5))\n      [0, 5, 10, 15, 20, 25]\n      >>> list(range(0, 10, 3))\n      [0, 3, 6, 9]\n      >>> list(range(0, -10, -1))\n      [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]\n      >>> list(range(0))\n      []\n      >>> list(range(1, 0))\n      []\n\n   Ranges implement all of the *common* sequence operations except\n   concatenation and repetition (due to the fact that range objects\n   can only represent sequences that follow a strict pattern and\n   repetition and concatenation will usually violate that pattern).\n\nThe advantage of the "range" type over a regular "list" or "tuple" is\nthat a "range" object will always take the same (small) amount of\nmemory, no matter the size of the range it represents (as it only\nstores the "start", "stop" and "step" values, calculating individual\nitems and subranges as needed).\n\nRange objects implement the "collections.abc.Sequence" ABC, and\nprovide features such as containment tests, element index lookup,\nslicing and support for negative indices (see *Sequence Types ---\nlist, tuple, range*):\n\n>>> r = range(0, 20, 2)\n>>> r\nrange(0, 20, 2)\n>>> 11 in r\nFalse\n>>> 10 in r\nTrue\n>>> r.index(10)\n5\n>>> r[5]\n10\n>>> r[:5]\nrange(0, 10, 2)\n>>> r[-1]\n18\n\nTesting range objects for equality with "==" and "!=" compares them as\nsequences.  That is, two range objects are considered equal if they\nrepresent the same sequence of values.  (Note that two range objects\nthat compare equal might have different "start", "stop" and "step"\nattributes, for example "range(0) == range(2, 1, 3)" or "range(0, 3,\n2) == range(0, 4, 2)".)\n\nChanged in version 3.2: Implement the Sequence ABC. Support slicing\nand negative indices. Test "int" objects for membership in constant\ntime instead of iterating through all items.\n\nChanged in version 3.3: Define \'==\' and \'!=\' to compare range objects\nbased on the sequence of values they define (instead of comparing\nbased on object identity).\n\nNew in version 3.3: The "start", "stop" and "step" attributes.\n',